| Agentic AI+JAVA | 6+Yrs Exp |
| 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, GitHub 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, GitHub Enterprise, Harness/GitHub 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 |
Mandatory Skills: Agentic AI .
Experience: 5-8 Years .
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