AI Engineer

Role: AI Engineer 

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.