Emily Chapman
Consultant
AI Engineer
Greater London, South East, England
Apply by 15 Apr 2026
Competitive
Job Ref.: BH-57019
Job Description
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.
- 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.