Job Summary

To understand client requirements and propose solution pertaining to respective domain area.HCLTech is looking for a hands-on AI Builder to implement Generative AI solutions on the OpenAI platform under the direction of the Enterprise AI Architect. This role sits at the build layer of GenAI delivery: taking approved architecture and designs and turning them into working, tested, production-ready components — RAG pipelines, agents, copilots, and integrations — while following the security, Responsible AI, and operational standards set at the architecture level. You will work closely with the Enterprise AI Architect and delivery engineering teams to ship reliable GenAI features quickly and to a consistent quality bar

Key Responsibilities

• Build and implement Generative AI solution components (RAG pipelines, agentic workflows, copilots, chat/assistant interfaces) on the OpenAI API / Azure OpenAI Service, per architecture provided by the Enterprise AI Architect.
• Develop and tune prompts, function/tool-calling definitions, and context/retrieval pipelines to meet accuracy, latency, and cost targets.
• Integrate GenAI components with enterprise systems, data sources, and vector databases, following approved data-access and security patterns.
• Implement guardrails, content filters, and evaluation harnesses to support Responsible AI and quality requirements defined by the architecture team.
• Write unit, integration, and evaluation tests for GenAI features; track accuracy, hallucination rate, and other quality metrics.
• Support production readiness: contribute to LLMOps pipelines, logging/observability hooks, cost tracking, and deployment scripts under architect guidance.
• Troubleshoot and resolve issues in existing GenAI solutions, including prompt regressions, latency, and integration failures.
• Document build decisions, prompt/version history, and configuration details to keep solutions maintainable and auditable.
• Collaborate with the Enterprise AI Architect on feasibility checks, effort estimates, and technical trade-offs during solution design.
• Reuse and extend approved architecture patterns and reference implementations rather than building one-off solutions.

Skill Requirements

• Experience delivering GenAI features in a client-facing systems integrator / consulting environment.
• Exposure to multi-agent frameworks or orchestration tools beyond a single-agent RAG pattern.
• Familiarity with LLMOps/MLOps tooling (e.g., MLflow, Azure ML, evaluation frameworks) for monitoring models in production.
• Experience with GitHub Copilot or similar AI-assisted engineering tools in the SDLC.
• Basic understanding of Responsible AI concepts (bias, fairness, hallucination mitigation) beyond implementation-level guardrails.

Other Requirements