Machine Learning Engineer
Modulai AB · Stockholm · Tills vidare
Modulai AB is looking for a **Machine Learning Engineer** to join its specialized team working across varied external client engagements and internal projects. The role takes end-to-end responsibility for ML solutions across diverse domains. Core duties include analyzing client challenges, reformulating them into machine learning problems, and handling dataset creation, feature engineering, and model validation. Engineers develop, fine-tune, and evaluate ML and LLM models, build data pipelines, develop APIs, and handle production deployment, monitoring, and performance optimization in collaborative project teams. Candidates must hold an **MSc or Ph.D. in a quantitative field**. Required competencies include an excellent understanding of broad machine learning and deep learning algorithms, including LLMs, strong Python development skills aligned with software engineering best practices, and **production deployment experience** with ML and LLM models. Advantageous knowledge includes transformer architectures, prompt engineering, RAG, CI/CD, Kubernetes, serverless infrastructure, and vector databases such as Pinecone, Redis, or Elasticsearch.
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/