Senior Data & AI Scientist

Lloyds Banking Group Hyderabad, Telangana, India

Financial Services · 10,001+ employees

Oct 14 Closes in 1d

Principal (10+ yrs) Full-time India

Create a free account to apply — email only, no card. You can also save this posting or score it against your profile with AI.

Applying here? Try the free cover letter tool — paste this posting and your résumé, no account needed.

About the role

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

What they look for

Python SQL Docker Kubernetes Git CI/CD Azure ML GCP Vertex AI LLM RAG MLflow Kubeflow Airflow FastAPI Vector databases LangChain

Requirements

Requires 7–12 years of experience in software, ML, or AI with proven technical leadership and production delivery. Candidates must have strong Python skills and hands-on experience with cloud AI stacks, MLOps, and LLM fundamentals.

Benefits

Flexible working

Full description

End Date

Thursday 22 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.