End Date
Thursday 30 July 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 D
Location: Hyderabad – Lloyds Technology Centre
Function: Lending & Working Capital Platform
Experience: 4–6 years (software/ML/AI); proven production delivery, hands on GenAI and Agentic AI experience
Location: Hyd
Mode: Hybrid
YOE: 4-6
Job Description
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.
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.
