Junior AI/ML Engineer

About the role

We're looking for a Junior Machine Learning Engineer to help us deliver projects rapidly. You'll report to and be mentored by a senior team member. This is a hands-on role from day one, working on real projects that make a tangible impact.

Requirements

• BSc in Computer Science, Software Engineering or equivalent
• MSc in Computer Science, Data Science, AI or equivalent
• Required Skills:
• Solid software engineering fundamentals (OOP, Git, concurrency, parallelism)
• Proficiency in Python
• Understanding of LLM system design (RAG, agents, etc.)
• Knowledge of ML system design (pipelines, training/inference techniques)
• Excellent English communication skills
• Experience in non-academic projects (jobs, internships or similar)
• Previous LLM projects (academic or otherwise)
• Exposure to AI features in cloud platforms (Sagemaker, Bedrock, Vertex AI)
• Experience working in large codebases
• Why TensorOps?
• Fully remote (legal residence in Portugal required)
• Real-world projects, rapid feedback loops, and measurable impact
• Mentorship from engineers who have shipped ML systems at scale
• Competitive compensation and growth opportunities - your growth will be based on ownership and performance rather than periodic reviews (which we still do)
• Compensation & Perks:
• Yearly salary: €30,000-35,000
• Travel expenses allowance
• Urban Sports Club membership
• Free Professional Certifications

Benefits

Additional Information

Build the Next Generation of AI Products with TensorOps TensorOps is an applied machine learning and artificial intelligence studio helping organizations worldwide plan, design, train, and deploy production-grade ML systems. Our clients range from NASDAQ-listed enterprises to seed-stage startups. Projects span from small proofs-of-concept to multi-year strategic initiatives. What We're Working On: Generative AI applications: Chatbots and Agents Traditional Machine Learning: Time Series Forecasting, AdTech, Computer Vision, etc. MLOps: Improving ML pipelines at scale Core Stack: As we work with many clients, our stack varies, but we often use: Python APIs: FastAPI Containerization: Docker, Kubernetes Model Training & Serving: LightGBM, CatBoost, PyTorch, HuggingFace Data Engineering: Pandas, Polars LLM Frameworks: LangChain, LangGraph Observability: MLFlow, Langfuse Cloud Platforms: AWS, GCP Search : Elasticsearch, OpenSearch, Solr

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