Mid-Level AI/ML Engineer

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

• Conversational copilots that assist knowledge workers

• Autonomous research agents for market-leading platforms

• Decision-support tools for healthcare, finance, and e-commerce

Core Stack

• Python, FastAPI, Docker

• TensorFlow, LightGBM, CatBoost

• Open-source & commercial LLMs

• LangChain / LangGraph, Langfuse, MCP

• MLFlow, Kubeflow

• AWS and GCP

The Role

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.

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.

About You

• 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.

Why TensorOps

• • High-Impact Projects: Work on challenging, real-world problems for industry-leading clients, seeing your work move from concept to production.

  • Expert Collaboration: Join a team of experienced ML engineers and researchers. We foster a culture of deep collaboration and knowledge sharing.

  • Career Growth & Ownership: We offer competitive compensation and provide clear paths for career progression. Take ownership of critical systems and grow into a senior role.