Job Description
The role
We’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 Q&A (RAG), summarisation, classification, extraction, and workflow/agent automations.
- Own evaluation & 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 & 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 use
- Python (pandas, PyTorch/Transformers), SQL
- LLM frameworks: LangChain, LlamaIndex (or similar)
- Vector DBs: FAISS / pgvector / Pinecone (or similar)
- Cloud & Dev: Azure/AWS/GCP, Docker, REST APIs, GitHub Actions/CI
- Data & MLOps: BigQuery/Snowflake, MLflow/DVC, dbt/Airflow (nice to have)
- Front ends (for internal tools): Streamlit / Gradio / basic React
Must-have experience
- 7+ 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 & SQL; solid software engineering habits (testing, versioning, CI/CD).
- Practical LLM skills: prompt design, RAG, tool/function calling, evaluation & 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 have
- Experience in regulated or public-sector-like environments.
- Azure OpenAI / Vertex AI / Bedrock; lightweight fine-tuning/LoRA.
- Front-end skills to craft usable internal UIs.
How to apply
Send 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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