AI Engineer (Biometrics and Data Science)

The AI Engineer will design, implement, and operationalize domain-specific AI agents. You will develop prompt strategies, retrieval/grounding pipelines, and guardrails, and build integrations to repositories, metadata stores, and execution environments with full provenance and traceability. The role establishes robust evaluation frameworks (automated tests, conformance checks, human-in-the-loop reviews) and optimizes model selection, performance, cost, and reliability for production use. Close collaboration with statistical programmers, statisticians, data scientist and platform engineers ensures agent behavior reflects standards, regulatory expectations, and real user needs.

Typical Accountabilities:

• Design, build, and iterate AI agents for the department, for example ADaM code generation agent, SAP generation agent, etc. combining LLMs with deterministic tooling, templates, and validation checks.
• Develop prompt strategies, retrieval and grounding pipelines (e.g., standards libraries, controlled terminology, study specifications), and guardrails for safe, compliant outputs.
• Implement evaluation frameworks for generated code (unit tests, statistical checks, conformance to standard, reproducibility) and establish quality metrics/KPIs.
• Build adapters that integrate agents with code repositories, metadata stores, execution environments, and review/approval workflows; enable provenance and traceability.
• Fine‑tune or customize models where appropriate (e.g., instruction tuning, adapters), and manage model selection, versioning, and inference optimization.
• Collaborate with statistical programmers, data scientist, and statisticians to capture requirements, encode domain logic, and incorporate feedback into agent behavior.
• Partner with full‑stack and DevOps engineers on deployment, monitoring, cost/performance tuning, and reliability in production environments.
• Maintain rigorous documentation of model behavior, data sources, prompt templates, evaluation results, and change control to support audits.

Education, Qualifications, Skills and Experience:

• Master’s degree or above in Computer Science, Data Science, Applied Mathematics, or related field, or equivalent practical experience.
• 3–5 years of experience building AI/ML or NLP applications, including production-grade systems using large language models or sequence models.
• Strong programming skills in Python, with experience in building services and pipelines (e.g., FastAPI, LangChain/LlamaIndex or equivalent frameworks).
• Experience with prompt engineering, retrieval‑augmented generation, and tool/function calling; ability to design deterministic post‑processing and validators.
• Familiarity with software engineering best practices: version control, testing, CI/CD, containerization, and observability for ML applications.
• Experience evaluating generative systems (human‑in‑the‑loop review, rubric design, offline/online metrics, A/B tests) and implementing safety/guardrail mechanisms.
• Ability to translate domain requirements into model capabilities and to communicate tradeoffs among quality, cost, latency, and interpretability.
• Experience integrating LLMs with code generation workflows and execution sandboxes, including static analysis and auto‑testing for generated code.
• Exposure to regulated environments (GxP, CSV) and audit-ready documentation practices; understanding of data privacy and security principles.
• Experience with vector databases, embeddings, and knowledge graph/RAG techniques; model optimization (quantization, distillation) and prompt versioning.
• Familiarity with MLOps for generative AI (model registries, feature/knowledge stores, inference gateways) and cost/performance monitoring.

Date Posted

09-4月-2026

Closing Date

29-7月-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.