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
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.
Preferred Qualifications:
BSc in Computer Science, Software Engineering or equivalent
MSc in Computer Science, Data Science, AI or equivalent
Understanding of LLM system design (RAG, agents, etc.)
Knowledge of ML system design (pipelines, training/inference techniques)
Excellent English communication skills
Nice to Have:
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)