Senior Data & AI Scientist

End Date
Sunday 04 October 2026
We Support Flexible Working – Click here for more information on flexible working options
Flexible Working Options
Hybrid Working
Job Description Summary
AI Engineer – Grade E (Senior Level)
Location: Hyderabad – Lloyds Technology Centre
Function: Chief Data & Analytics Office (AI CoE)
Experience: 7–12 years (software/ML/AI); proven production delivery and technical leadership
Role Purpose
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.
Job Description
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 (Azure ML or 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.