London, United Kingdom | Posted on 12/09/2025We’re hiring a Data Scientist with strong Generative-AI experience to design, build, and ship AI-powered tools end-to-end. You’ll work in a small, multi-disciplinary team and take ownership from discovery to deployment: scoping use-cases, building prototypes, hardening them for production, and putting the right evaluation and governance around them.

What you’ll do

Build GenAI tools end-to-end (independently): chat/assistants, document QandA (RAG), summarisation, classification, extraction, and workflow/agent automations.

Own evaluation and safety : create offline/online eval sets, measure faithfulness/hallucination, bias, safety, latency and cost; add guardrails and red-teaming.

Productionise : package as services/APIs or lightweight apps (e.g., Streamlit/Gradio/React), containerise, and integrate via CI/CD.

Data pipelines : design chunking/embedding strategies, pick vector stores, manage prompt/versioning, and monitor drift and quality.

Model strategy : select and mix providers (hosted and open-source), fine-tune where it’s sensible, and optimise for cost/perf/privacy.

Stakeholder enablement : translate problems into measurable KPIs, run discovery, document clearly, and hand over maintainable solutions.

Good practice : apply data ethics, security and privacy by design; align to service standards and accessibility where relevant.

Tech you’ll likely useLLM frameworks : LangChain, LlamaIndex (or similar)

Cloud and Dev : Azure/AWS/GCP, Docker, REST APIs, GitHub Actions/CI

Data and MLOps : BigQuery/Snowflake, MLflow/DVC, dbt/Airflow (nice to have)

Front ends (for internal tools) : Streamlit / Gradio / basic React

Must-have experience7+ years in Data Science/ML, including hands-on delivery of GenAI products (not just PoCs).

Proven ability to ship independently : from idea ? prototype ? secure, supportable production tool.

Strong Python and SQL ; solid software engineering habits (testing, versioning, CI/CD).

Practical LLM skills: prompt design, RAG , tool/function calling, evaluation and guardrails , and prompt/model observability.

Sound grasp of statistics/experimentation (A/B tests, hypothesis testing) and communicating impact to non-technical audiences.

Data governance, privacy and secure handling of sensitive data.

Nice to haveExperience in regulated or public-sector-like environments.

Front-end skills to craft usable internal UIs.

How to applySend your CV (referencing DS-GENAI ) to the Recruitment Team. Shortlisted candidates will complete a brief technical exercise or portfolio walk-through focusing on a GenAI tool you built and shipped .

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