Design and build LLM-powered agentic systems using frameworks such as LangGraph and LangChain
Develop and optimise RAG pipelines, tool-using agents, and multi-step workflows with appropriate guardrails and validation
Monitor and evaluate model and agent performance using appropriate ML metrics (e.g. precision, recall) and production monitoring tools
Maintain and improve traditional ML models alongside newer GenAI capabilities
Partner closely with stakeholders to frame problems, define success metrics, and deliver measurable business impact
Take ownership of technical initiatives, driving delivery from ideation through to production and iteration
Contribute to our wider AI & ML community through knowledge sharing, experimentation, and continuous learning

Strong experience in Machine Learning with solid foundations in the ML lifecycle, evaluation methodologies, and statistical thinking
Hands-on experience with GenAI, Large Language Models, and NLP techniques, including RAG and agent-based systems
Proficient in Python and common ML libraries (e.g. PyTorch, scikit-learn, XGBoost, Pandas)
Experience deploying and operating ML or AI systems in production environments
Understanding of ML Ops and DevOps principles (e.g. Docker, CI/CD, infrastructure as code)
Experience working with cloud infrastructure (preferably AWS)
Take ownership of technical tasks and proactively drive solutions forward

Machine Learning Engineer

Key skills