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Lbg

Senior Data & AI Scientist

Hyderabad Knowledge Park Tower 2

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hirly's read of this role

Seniority
Senior
Country
IN
Work mode
On-site / unstated
First seen by hirly
4 Sept 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

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.
Original posting on Lbg's site ↗

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