AI Summary
Design, build, and maintain production-grade ML systems. Develop and fine-tune generative AI models. Collaborate with senior engineers and client stakeholders.
Key Highlights
Design, build, and maintain production-grade ML systems
Develop and fine-tune generative AI models
Collaborate with senior engineers and client stakeholders
Take ownership of key components of ML platform
Ensure code quality, performance, and scalability
Technical Skills Required
Benefits & Perks
Yearly salary: €48,000-60,000
Fully remote
Travel expenses allowance
Urban Sports Club membership
Free Professional Certifications
Job Description
Build the Next Generation of AI Products with TensorOps
TensorOps is an applied-machine-learning studio that helps organisations across Europe and North America design, train, and deploy production-grade GenAI systems. Our team blends research depth with pragmatic engineering, and we’re looking for experienced engineers to help us build and scale our solutions.
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
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
As a Mid-Level Machine Learning Engineer, you will be a key contributor to our project teams, taking ownership of core components and shipping robust AI/ML systems. This is a hands-on role from day one, working on real projects that make a tangible impact.
You will:
- Design, build, and maintain production-grade ML systems, from data ingestion and processing to model deployment and monitoring.
- Develop and fine-tune generative AI models, including LLMs, for specialized tasks. You'll move beyond prototyping to build robust, scalable solutions.
- Architect and implement reliable data pipelines and low-latency inference services using our core stack (FastAPI, Docker, Kubeflow, AWS/GCP).
- Collaborate with senior engineers, researchers, and client stakeholders to translate business problems into technical solutions and deliver tangible value.
- Take ownership of key components of our ML platform, ensuring code quality, performance, and scalability.
- 3+ years of professional experience in a software engineering or machine learning role.
- Strong proficiency in Python and its data science ecosystem (e.g., Pandas, NumPy, Scikit-learn).
- Hands-on experience building and shipping models using at least one major ML framework.
- Proven experience with the practical application of Large Language Models (LLMs). Familiarity with frameworks like LangChain/LangGraph and retrieval-augmented generation (RAG) is a significant plus.
- Solid understanding of software engineering best practices, including version control (Git), testing, CI/CD, and containerization (Docker).
- A BSc/MS in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
- 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)
- Yearly salary: €48,000-60,000
- Travel expenses allowance
- Urban Sports Club membership
- Free Professional Certifications