AI Engineer

• *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.