14 hours ago
London, United KingdomMid Level / Senior
Responsibilities
- Design, train, evaluate, optimise, and productionise predictive, temporal, embedding, NLP, and sequence models.
- Build reproducible training, evaluation, monitoring, feature engineering, and model-debugging pipelines.
- Own schema-aware data flows, validate and version datasets, and manage database schemas, SQL optimisation, indexing, and partitioning.
- Lead modelling and data engineering components of client projects, acquire data from client source systems, and build and validate cohort logic.
- Build and integrate embedding pipelines, RAG workflows, text analysis models, and agentic and multicomponent AI systems.
- Develop containerised model services and configure OneView components linked to modelling outputs.
- Troubleshoot complex modelling and pipeline issues and collaborate with clients, data scientists, engineers, and consultants.
- Act as a machine learning and AI subject-matter expert, mentor colleagues, and produce documentation, templates, and reusable components.
Requirements
- 3–5+ years of experience in machine learning engineering, taking models from development into production.
- Strong Python engineering skills and experience with modern machine learning frameworks.
- Practical experience training and evaluating tree-based, temporal, embedding/NLP, or LLM-based models.
- Experience building reproducible training and evaluation pipelines and containerising and deploying models with Docker and FastAPI.
- Strong SQL skills and experience with relational databases, schemas, data transformations, and preparing data for model training and scoring.
- Hands-on experience with embeddings, vector databases, RAG-style workflows, NLP, or sequence models.
- Experience delivering technical work to clients or stakeholders, including defining data requirements and discussing modelling decisions.
- Ability to explain technical concepts clearly and collaborate with data scientists, engineers, and consultants.
- Bonus experience with dbt, Azure ML, AKS or similar cloud environments, and public-sector datasets or analytical workflows.
Benefits
- 12-month maternity leave cover opportunity.
- Hybrid London role requiring office attendance at least 1–2 days per week, with remote work available except for face-to-face team and client meetings.
- Some travel for on-site client engagements is required.
- Flexible hours, annual salary review, training and development opportunities, 25 days of annual leave plus bank holidays, company pension, private medical insurance, enhanced parental leave, cycle-to-work scheme, flu vaccinations, eye tests and VDU glasses contributions, employee assistance, mental health and wellbeing support, remote GP access, counselling or therapy, physiotherapy, and medical second opinions.
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