Machine Learning Engineer - Sweden at Modulai

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