Agentic AI+JAVA

Role purpose

Lead the evolution of Pioneer's agentic software delivery lifecycle. Combine senior enterprise engineering judgement with hands-on implementation of practical agentic capabilities and accountable ownership of their adoption.

What the client is asking for

The client needs a solid engineering thought leader who has crossed into next-generation agentic SDLC.
Proactively identify additional AI capability for Pioneer and get up to speed on relevant ventures across LBG.
This is an enterprise engineering leadership role, not an AI research or model-training specialist position

Required experience

Senior, hands-on enterprise engineering leadership across complex SDLC workflows, shared platforms and governed releases. Own capabilities through production support and demonstrate cross-functional adoption and mentoring.

Agentic engineering

Python automation/orchestration, RAG, context preparation, MCP, tool invocation and
agent workflows. Evaluate retrieval quality, grounding, reproducibility and failure; retain
accountable human judgement

Core tools & controls

VS Code, Git Hub Copilot, approved agents/models and OpenAPI/API integrations.
Responsible AI:



approved data access, provenance, output validation and meaningful
review. No unapproved framework is mandated

Enterprise integration

IDP, Backstage, Git Hub Enterprise, Harness/Git Hub Actions, GCP, containers and Kubernetes at integration depth. Work with Terraform-managed configuration and RTL controls

Engineering credibility

Coding, testing and architecture contributions; understand Java/Quarkus, React/Node.js, Domain-Driven Design, Kafka, event sourcing and CQRS sufficiently to assess agentic work and operational risks

Evidence to request

Ask for an adopted enterprise improvement they implemented, a RAG/agent failure and its evaluation, and controls protecting secure operation. Probe production ownership, architecture influence and mentoring.

Suggested practical assessment

Review an enterprise workflow and prototype one bounded improvement using Python and appropriate RAG/MCP integration. Explain evaluation, responsible AI controls, maintainability and rollout; first assess opportunities to reuse existing LBG capabilities

📌 Agentic AI + Java (India)
🏢 GK HR Consulting India Private
📍 India