Civilingenjör inom Farkostteknik - Lediga jobb

Mycronic is a global high-tech company whose innovative solutions have been advancing electronics technology for over 40 years. Today we continue to grow and serve customers in an expanding variety of industries. What we do impacts the future of technology, and in turn, the way we live our lives tomorrow. We’re looking for people to join our talented, motivated and friendly team on the cutting-edge. Here, in an open, collaborative and flexible environment, you’ll work alongside leading industry experts and take on challenging projects that bring tomorrow’s electronics to life. If you have several years of work experience within software development and embedded systems and wants to be part of a growing company with world leading products, this is for you!

About the role

Mycronic has experienced strong growth in recent years, both organically and through acquisitions. With headquarters in Sweden, Mycronic is a global organization with subsidiaries, agents, and distributors in over 50 countries. Mycronic is divided into four divisions, and in this role, you will belong to PCB Assembly Solutions (PA). PA is a leading provider of equipment and solutions for surface mount technology (SMT) to electronics manufacturers who require rapid production adjustments while maintaining high efficiency. PA delivers systems for circuit board assembly, including both complete line solutions and individual systems for specific process steps, as well as advanced software for assembly process management and connected factories. In this role, you will be part of a team of 7 team members and collaborate with several functions across the company, such as architects, project managers and other software teams within for example AI.

You are offered

• The opportunity to work in a multi-cultural company with knowledgeable, friendly and goal-oriented colleagues.
• To work in an exciting company in a growth phase, where you can make a real impact in a fun and dynamic environment. Mycronic is a global company, yet small enough for you to truly make a difference.
• The opportunity to work with world-leading products. The future of electronics production is increasingly complex. Denser boards, a wider range of miniaturized components, more NPIs and erratic production schedules. To address these challenges Mycronic has developed the industry’s fastest, most flexible and most precise jet printing and solder paste dispensing systems. Mycronic jet printing technology makes it possible to dispense solder paste for the most challenging circuit boards and components, with micrometer accuracy, maximum speed and perfect quality solder joints. Read more here: https://www.mycronic.com/product-areas/pcb-assembly/products/smt/jet-printing/.
• Mycronic has a large software organization which will provide you with optimal opportunities to develop your knowledge in various areas of computer science – such as software architecture for robotics, machine learning, AI, optimization, and computer vision.

Work tasks

You will work in a broad developer role where you will truly have the opportunity to influence the final solution of the products. The Mycronic Jet Printer, which is the product area you will be developing the software for, includes electronics, mechanics, and software, creating exciting opportunities for those who want to work with complex software development involving multiple technical disciplines. We are continuously working to enhance our products; user-friendliness, particularly in terms of automation and user interfaces, which will also be part of your development work. Your primary focus will be on improving the products, with opportunities to contribute your ideas and collaborate as a team to create the best solutions. This may involve anything from adding capability features and reducing energy consumption to updating the GUI. Mycronic has both R&D, lab, and production in-house, meaning you will work close to the products you develop, which also gives you the opportunity to see the "real world" results of your work.

We are looking for

This is a role for someone with a strong background in software development and an interest in understanding the entire product and an ambition to grow into a software architect role. As a software developer you will have a key role in the product development since this is where all the technology in the machine comes together to provide the system functionality that meets the user. You will be involved in everything from requirements gathering to design, architecture and development and collaborate with several stakeholders. As a person, we see it as particularly important that you are goal- and detail-oriented and have strong collaboration skills. You also have a will to contribute with your ideas, and just like your colleagues, you always strive to develop yourself and contribute to the team´s collective growth. We expect you to make a strong contribution by adding new functionality, maintaining high quality and by helping to introduce new technology and processes to increase team efficiency.

You likely have:

• A bachelor’s or master’s degree in computer science, electronics, mechatronics, or a similar field.
• Strong software development experience with the capability and ambition to take on broader architectural responsibilities in a few years.
• Knowledge in Java.
• Strong knowledge of software development in Linux.
• Experience of working with software systems that interact with embedded software and hardware.
• Strong knowledge in English and good knowledge in Swedish (or have the desire to learn)

It is considered advantageous if you have:

• Knowledge in C and/or C++

Other Information

• Start date: By agreement
• Employment type: Permanent
• Scope: Full-time
• Location: Kista
• Contact Information: Senior Recruitment Consultant Johanna Sörell, johanna.sorell@academicwork.se
• The recruitment process is handled by Academic Work, and Mycronic requests that all questions regarding the process goes to Academic Work.

Our recruitment process

• Problem-solving and personality test
• Phone interview with Academic Work
• In-depth interview with Academic Work
• Interviews with Mycronic
• Reference check + decision

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Modulai works with fish, trains, clothes, money, pets, office spaces, sound sensors and much more. If there is data, we do ML (Machine Learning) on it.

Our team consists of devoted ML engineers with strong track records from some of Sweden’s most successful startups. We work on project basis and take end-to-end responsibility. We love ML and we think that the best way for us to expand our knowledge is to be exposed to a diversified set of challenging and fun projects.

MACHINE LEARNING ENGINEER

As a member of the ML-team you will be working with a broad range of problems with one common denominator – ML will be the key ingredient. The projects could be external as well as internal – and in all cases – delivery is central.

You will have to analyze the problem at hand, come up with a solution strategy and execute on it. This typically entails gaining an in-depth understanding of the challenge, understanding the available data and then re-formulating it as a ML problem. It requires openness, creativity and an eagerness to learn new methodology and exploring new terrains.

We frequently attack these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us.

Our Stack

• Python / R – standard open-source libraries
• Scikit-learn and various specialized Python and R ML libraries
• Large Language Model (LLM) frameworks such as LangChain/LlamaIndex, LangGraph, CrewAI
• Cloud platforms such as AWS, GCP, and Azure
• CI/CD: DVC, Github Actions, Sagemaker/VertexAI/AzureML,
• Relational database management systems
• MLOps and LLMOps tools for model deployment and monitoring.
• Software engineering best practices, including testing, version control (Git), and containerization (Docker, Kubernetes)
• Orchestration: Airflow, AWS Step functions, etc Engineering/LLM/deployment: Kubernetes, docker, terraform

Responsibilities

• Analyzing and planning problems, solutions, and delivery with stakeholder managment, and communication with client
• Preprocessing, feature engineering, and dataset creation
• ML and LLM model development, fine-tuning, and evaluation
• Validation of results and model interpretability
• Building and optimizing data pipelines and ML/LLM infrastructure
• Developing APIs and integrating ML models into production systems
• Ensuring scalability, monitoring, and performance optimization of deployed models

Background & Skills

• MSc or Ph.D. in a quantitative field
• Excellent understanding of a broad set of ML and deep learning algorithms, including LLMs
• Strong software development skills in Python and experience with software engineering best practices
• Experience deploying ML and LLM models into production environments
• A passion for lean, clean, and maintainable code
• The desire to grow and to share insights with others

Helpful Knowledge

• Deep learning frameworks and transformer-based architectures
• LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG)
• Data pipelining and ML/LLM infrastructure best practices
• DevOps experience, CI/CD, Kubernetes, and serverless architectures
• Experience with vector databases e.g (Pinecode, redis, and ElasticSearch) for LLM applications

About Team Modulai

At Modulai we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis. We work for clients, as part of the core team in startups where we have long-time engagement as well for large enterprises transforming ways of working.

Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but actually, never do it because we always work in teams. If there is data, we will do ML on it!

Apply here: https://modulai.teamtailor.com/

Ready to take ownership of real hardware systems and see your designs come to life? We’re looking for a hands-on System Engineer who thrives in bridging design, testing, and integration.

About the role

In this role, you will work broadly with system engineering focused on hardware and electronics, playing a key part in designing and developing low-voltage cabinets used across our drivetrain and overall solutions. You will be responsible for everything from requirements gathering and system design to component selection, integration, and testing.

A significant part of the role is hands-on, including building and preparing test setups for EMC testing and monitoring systems. You will create electrical schematics and circuit diagrams, validate functionality through testing, and ensure proper documentation of requirements, acceptance criteria, and test results. The role also involves supporting certification processes and collaborating closely with project managers and other teams, particularly around cable interfaces.

You will have a high level of ownership and responsibility, with the freedom to plan your work independently. Clear communication and reliability in delivering tasks are key to succeeding in this position.

We believe you have experience with:

• System engineering within hardware and/or electronics

• Designing electrical systems and selecting suitable components

• Creating electrical schematics and circuit diagrams

• Hands-on work such as building and testing setups (e.g. EMC testing)

• Requirements analysis and system design

• Testing, validation, and documentation of results

• Working with low-voltage systems, cabinets, or similar hardware environments

• Collaborating cross-functionally and managing interfaces between teams

• Taking ownership of your work and communicating progress clearly

About Astek:

Astek has always been driven by a strong sense of curiosity and a desire to explore the possibilities of technology. This mindset forms the foundation of everything we do – from our work with innovation and continuous development to the long-term client relationships we build and the value we deliver in every assignment. With us, you will have the opportunity to grow and develop alongside some of the industry’s most experienced engineers and technical specialists.

We are a company where innovation, collaboration, and development are at the core. At Astek, you will have the opportunity to work on exciting and challenging projects in an environment where your expertise and ideas are truly valued. We offer:

• An inspiring workplace where technology and creativity come together

• Opportunities to collaborate with leading players within the industry

• Continuous competence development and clear career opportunities

• A strong company culture where we enjoy working together and support one another

• Competitive benefits, including a wellness allowance of SEK 5,000

Other information:

As part of our recruitment process, identity verification and background checks may occasionally be conducted. Valid work permit for Sweden is required.

We recruit on an ongoing basis, so do not hesitate to submit your application

Talent Acqusition

Jannica Niklasson Roos