Senior AI/ML Engineer - Grocery Forecasting & Personalization

MorgansTek; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

MorgansTek is seeking an experienced ML/AI engineer in Greater London to build, train and deploy production-ready models for forecasting, recommendations, and in-store analytics. You will design LLM-powered features and ensure end-to-end production readiness from data to monitoring.

Ideal candidates have 5+ years of hands-on ML, strong Python and PyTorch/scikit‑learn, and experience with embeddings and retrieval‑augmented generation. Creative, experiment-driven, and results-focused.

Qualifications

• 5+ years in ML/AI engineering with models shipped to production.
• Strong Python and the modern ML stack (PyTorch, scikit-learn, etc.).
• Hands-on experience with LLMs, embeddings, and retrieval-augmented generation.
• Able to balance research curiosity with practical, shippable outcomes.
• Creative, experimental, and excited to try new approaches.

Responsibilities

• Build, train, and deploy models for forecasting, recommendations, anomaly detection, and more.
• Design and ship LLM-powered features — assistants, document and image processing.
• Take models all the way to production: serving, monitoring, evaluation, and iteration.
• Work with the data team to make sure the right data is available and clean.
• Stay on top of a fast-moving field and bring the best of it into the work.

Skills

ML/AI engineering

Python

LLMs

Embeddings

Retrieval-augmented generation

Production deployment

Creative experimentation

Tools

PyTorch

scikit-learn

Cloud deployment

Job description

MorgansTek is seeking an experienced ML/AI engineer in Greater London to build, train and deploy production-ready models for forecasting, recommendations, and in-store analytics. You will design LLM-powered features and ensure end-to-end production readiness from data to monitoring.

Ideal candidates have 5+ years of hands-on ML, strong Python and PyTorch/scikit‑learn, and experience with embeddings and retrieval‑augmented generation. Creative, experiment-driven, and results-focused.