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- Location
- Hyderabad
- Level
- Mid-level
- Experience
- 4–6 years
- Pay
- Not listed
- Workplace
- Hybrid
- Posted
- 4 days ago
- Status
- Open · checked 1d ago
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Skills mentioned in the posting
- LLM
- MLOps
- CI/CD
- RAG
- Microservices
- FastAPI
- Python
- SQL
- Docker
- Kubernetes
Friday 30 October 2026
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Job Description Summary
Location: Hyderabad – Lloyds Technology Centre
Function: Chief Data & Analytics Office (AI CoE)
Experience: 4–6 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.
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.