Role: AI Engineer Location: 2 days a week either in the Dublin or Cambridge Office Contract type: Inside IR35 contracting (6 months with view for multiple extensions) As an AI / GenAI Engineer, you will design, build, and govern applied AI solutions addressing scientific and operational challenges within a leading pharmaceutical organisation. You will own the end-to-end lifecycle — from rapid prototyping with research teams to production-grade, monitored, and auditable ML systems — and act as a technical authority on responsible AI. You will also contribute reusable patterns for RAG pipelines, agent orchestration, and model lifecycle management. Responsibilities Translate business and scientific needs into well-defined AI use cases with clear success metrics. Design and deploy end-to-end ML and GenAI pipelines, including data ingestion, feature engineering, inference, and monitoring. Build and optimise RAG systems (vector stores, chunking, embeddings, reranking) for enterprise knowledge bases. Productionise prototypes into maintainable services integrated with CI/CD and MLOps tooling. Deploy containerised models ( Docker, Kubernetes ) on cloud platforms — preferably Azure. Manage model lifecycle and governance : experiment tracking, versioning, performance evaluation, bias checks, and compliance (e.g., EU AI Act, GxP where applicable). Monitor production models for drift, degradation, and anomalies. Document architecture and operational processes to meet engineering and audit standards. Collaborate with data scientists, engineers, and domain experts; mentor junior team members; contribute to internal AI knowledge sharing. Skills & Experience 5–9 years in machine learning engineering, data science, or applied AI. Strong Python engineering with production-quality coding and testing practices. Experience building RAG systems using vector databases (e.g., Pinecone, Weaviate, pgvector, Azure AI Search). Hands-on GenAI experience : prompt engineering, LLM fine-tuning or RLHF, and agent frameworks (LangChain, LlamaIndex, AutoGen, etc.). Experience taking AI/ML systems from prototype to production on cloud platforms (Azure preferred; AWS/GCP acceptable). Familiarity with MLOps tooling (experiment tracking, model registries, feature stores, ML CI/CD). Understanding of responsible AI principles including explainability, fairness, and governance.