Production ML Engineer - LLMs, RAG & MLOps Champion

Leap29; Greater London; On-site; GBP 85,000 - 125,000; Full time

Job summary

Leap29 in London is seeking a hands‑on Machine Learning Engineer to design, build and ship production ML systems, including LLM and generative AI features. You will take models from prototype to production, collaborating with data scientists, product managers and software engineers.

You will design, train and deploy models, build scalable pipelines for training and inference, and ensure monitoring and MLOps best practices are in place across cloud platforms.

Qualifications

• 4+ years' experience as a Machine Learning Engineer or similar
• Strong Python skills and ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience deploying models to production on cloud platforms
• Solid MLOps understanding: Docker, orchestration, experiment tracking
• Good communication and collaboration with data scientists and engineers

Responsibilities

• Design, train, evaluate and deploy ML models including LLM-based solutions into production
• Build scalable data and ML pipelines for training, inference and monitoring
• Develop AI features such as retrieval-augmented generation (RAG), embeddings and fine-tuning
• Turn experimental notebooks into robust, production-grade code
• Monitor production models for drift, latency, cost and reliability
• Set up MLOps: CI/CD, experiment tracking, model versioning, automated testing
• Collaborate with product and engineering to translate business needs into technical solutions
• Keep up with AI developments and bring practical ideas to the team
• Contribute to responsible AI practices, including bias checks and data privacy

Skills

Python programming

Communication

Team collaboration

Problem solving

Education

Bachelor's degree in CS/ML or equivalent

Tools

Docker

Kubernetes

MLflow

Airflow

Cloud platforms (AWS/Azure/GCP)

Job description

Leap29 in London is seeking a hands‑on Machine Learning Engineer to design, build and ship production ML systems, including LLM and generative AI features. You will take models from prototype to production, collaborating with data scientists, product managers and software engineers.

You will design, train and deploy models, build scalable pipelines for training and inference, and ensure monitoring and MLOps best practices are in place across cloud platforms.