Machine Learning Engineer - Sweden
AI Tools
Tech Stack
Agent Workflow
LLM model development, fine-tuning, and evaluation using frameworks like LangChain, LlamaIndex, LangGraph, and CrewAI. Build RAG systems with vector databases (Pinecone, Redis, ElasticSearch) and deploy with LLMOps tools.
About the Role
Modulai is a leading machine learning agency founded in 2018. The team consists of devoted ML engineers with strong track records from some of Sweden's most successful startups. The company works on a project basis and takes end-to-end responsibility, helping organizations across healthcare, finance, retail, logistics, and manufacturing implement ML solutions at scale.
Position: Machine Learning Engineer - Sweden
Locations: Gothenburg and Stockholm, Sweden (Hybrid)
Type: Full-time
As an ML Engineer, you will work with a broad range of problems with one common denominator — ML will be the key ingredient. You will analyze problems, come up with solution strategies, and execute them.
Key Responsibilities:
- Analyzing and planning problems, solutions, and delivery with stakeholder management
- Preprocessing, feature engineering, and dataset creation
- ML and LLM model development, fine-tuning, and evaluation
- Cloud platform utilization (AWS, GCP, Azure)
- Validation of results and model interpretability
- Building and optimizing data pipelines and ML/LLM infrastructure
- Deploying ML and LLM models into production environments
- Production deployment and API integration
- Model monitoring and performance optimization
Requirements:
- MSc/Ph.D. in quantitative field
- 2+ years production ML experience
- Strong Python development with software engineering best practices
- Excellent understanding of a broad set of ML and deep learning algorithms including LLMs
- Production deployment experience
- Visa sponsorship not available — candidates must have existing work authorization for Sweden
Technology Stack:
- Python, R, Scikit-learn
- LLM frameworks: LangChain, LlamaIndex, LangGraph, CrewAI
- Cloud: AWS, GCP, Azure
- Tools: DVC, GitHub Actions, Docker, Kubernetes, Airflow
- Vector databases: Pinecone, Redis, ElasticSearch
- RAG, LLMOps tools for deployment/monitoring
- Transformer architectures
Benefits:
- Flexible work environment
- Health checks
- Annual ski trips
- Knowledge-sharing culture
- Exposure to cutting-edge AI problems