ai engineer

Lead end-to-end technical design and solution architecture for AI systems across enabling functions; Make and document model strategy, integration, and tooling decisions against product and compliance requirements; Identify and build shared components, reusable agent patterns, and governance instrumentation; Implement model routing, data residency, and hosting decisions that respect jurisdictional boundaries; Design and ship multi-agent LLM systems across providers with sovereignty-aware routing and human-gated controls; Build governed RAG pipelines with hallucination guards and immutable audit logging; Deliver proof-of-concepts in days with progression gates to pilot and production; Implement governance-as-code components, approval workflows, monitoring, oversight, routing layers, model and data cards, and audit trails; Engineer systems for data protection laws, AI regulations, and sector-specific operational resilience requirements; Build resilience through circuit breakers, provider fallbacks, graceful degradation, fail-safe defaults, failover paths and chaos testing; Own delivery from design through production, scaling, monitoring, and lifecycle management; Establish CI/CD, automated testing, performance monitoring, incident response automation, and capacity management; Build data pipelines, feature stores, and data products with quality, governance, lineage, and controls; Implement drift and bias monitoring, lead AI red-teaming, embed fairness and explainability pipelines, and produce assurance reporting; Adapt solutions across business functions and integrate external partners at a technical level; Run technical discovery, prototype quickly, iterate on UX, and measure adoption and impact through KPIs and KRIs; Represent delivery in architecture and AI leadership forums, contribute to enterprise governance and standards, and share reusable patterns., development, MLOps, fairness testing, explainability pipelines, AI red-teaming, assurance reporting, resilience engineering, classification and tiering logic, model registration, approval workflow automation, monitoring pipelines, lifecycle decommissioning controls, enterprise technology and data engineering partnerships, KPI/KRI design, UX-driven iteration., The role requires working from the office an average minimum of three days per week; Flexible arrangements are available around the office expectation; Technology directly supports life-changing medicines; Opportunities include experimentation with leading-edge platforms, collaboration with diverse experts, hackathons, and external partnerships; The company supports diversity, equality of opportunity, and inclusive hiring.

Built AI applications inside a large, regulated enterprise; Delivered AI solutions across HR, Finance, Procurement, Legal, Audit, or Compliance; Significant experience implementing governance controls and operational resilience in code and architecture; Ability to move from an idea to a working proof-of-concept in days; Deep expertise in AI governance, operational resilience, and regulatory compliance; Technical authority in multi-agent architecture, production coding, RAG pipelines, and governed AI applications; Experience implementing multi-jurisdictional data sovereignty, regulatory divergence, and cross-border AI governance requirements; Proficiency in foundation models, fine-tuning, multi-provider orchestration, RAG, and sovereignty-aware model routing; Leads through mentorship, code quality, and engineering standards while remaining a strong individual contributor; Nice to have: Shared libraries, reusable agent frameworks, governance middleware, common instrumentation, AI-assisted

AstraZeneca is a pharmaceutical manufacturing company that combines data, analytics, AI, modern engineering, and science to support the development of life-changing medicines and enable global business functions.