AI Engineer

Lead the design and delivery of enterprise-scale AI/ML solutions—including LLM/GenAI features—with strong focus on reliability, security, and compliance. Drive technical standards, mentor junior engineers, and collaborate with cross-functional teams to operationalise AI safely and efficiently.

Key Responsibilities

• AI Solution Design & Delivery:

Architect and implement advanced ML and GenAI systems; optimise for performance, cost, and scalability.

• Model Operationalisation (MLOps):

Build CI/CD pipelines, implement automated testing, and manage model lifecycle with MLflow or equivalent.

• LLMOps & GenAI:

Develop RAG workflows, embeddings, and vector indexes; enforce prompt safety, observability (latency, token usage, cost), and guardrails.

• APIs & Integration:

Expose models via secure microservices (FastAPI or similar); ensure RBAC/ABAC and audit logging.

• Governance & Compliance:

Embed AI ethics, regulatory standards, and security controls into all solutions.

Essential Skills

• Strong Python and software engineering discipline; working knowledge of SQL.
• Hands-on with Docker/Kubernetes and Git-based CI/CD (GitHub/Azure DevOps).
• Experience with cloud AI stacks (GCP Vertex AI), artefact registries, and secrets management.
• Deep understanding of LLM fundamentals (prompting, embeddings, RAG, guardrails).
• Familiarity with MLflow/Kubeflow, Airflow/Composer, and feature stores (e.g., Feast).

Desirable Skills

• Vector DBs (PGVector/Weaviate/Pinecone), LangChain/LlamaIndex.
• Observability tools (Prometheus/Grafana/OpenTelemetry) and model evaluation frameworks (Evidently, Ragas/TruLens).
• Secure engineering practices: tokenisation/masking, KMS/Key Vault, policy-as-code.