AI Engineer

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 life cycle - 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 life cycle 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 life cycle and governance: experiment tracking, versioning, performance evaluation, bias checks, and compliance (eg, 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 (eg, 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.

• 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 (eg, 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.