Senior Machine Learning Engineer

About the role

We are looking for an experienced Senior Machine Learning Engineer to join our Product team, in Chiswick, West London,

You will be responsible for building production-grade AI capabilities across the platform’s generative and agentic paradigms – from retrieval-augmented knowledge assistants and context-aware responses to multi-step agent workflows. The role combines strong machine learning and software engineering skills to deliver grounded, governed, and scalable solutions that move from Lab prototype to Factory production.

This is an excellent opportunity for an experienced engineer, looking for their next move in a global organization.

Key responsibilities

Design and implement ML, generative, and agentic AI solutions - RAG pipelines, prompt workflows, tool-calling agents, and predictive models

Build grounded retrieval over enterprise knowledge with source citation and tenant isolation

Integrate models via the model gateway, applying guardrails, PII redaction, and content safety on every request

Develop and maintain agent orchestration, memory, and human-in-the-loop escalation paths

Perform data preprocessing, feature engineering, prompt design, and evaluation using enterprise datasets

Deploy solutions through MLOps/LLMOps pipelines with monitoring, evaluations, and SLAs

Optimise models and prompts for accuracy, latency, cost, and groundedness

Run experiments, track metrics against golden sets, and iterate to improve quality

Collaborate with AIOps and Security to integrate solutions into CI/CD and production monitoring

Support responsible-AI practices, model cards, and version control for every release

About you

6+ years in AI/ML engineering or applied machine learning

Strong Python skills with scikit-learn, TensorFlow, PyTorch, or XGBoost, plus experience with LLM frameworks (LangChain/LangGraph) and RAG

Experience with cloud AI services (AWS Bedrock/SageMaker, Azure, or GCP) and vector stores

Proficiency in SQL and working with data warehouses/lakes and embeddings

Familiarity with MLOps/LLMOps, containerisation (Kubernetes), and CI/CD

Understanding of prompt engineering, evaluation harnesses, and guardrails

Strong grasp of ML theory, software engineering practices, and version control (Git)

Benefits

Competitive salary and incentive scheme

Warm, supportive, and open company culture

An opportunity to thrive in a global environment

Hybrid working: 3 days in the office

Birthday holiday and option to purchase additional annual leave

Comprehensive Benefits Package: Private Pension, Private Medical Insurance, Life Assurance and more