Job Summary

As a Forward Deployed Engineer, you operate at the front line of delivery ; embedded with the client, turning ambiguous business problems into working software, fast. You own the outcome end to end: conceptualize the solution, prototype it, integrate it into the client's real environment, harden it, and lead a small team to ship and sustain it. You are part builder, part consultant, and an engineer who uses AI/GenAI as a force multiplier for delivery and operational efficiency. You bring HCLTech's AI/GenAI capabilities to life inside the client's world, with Responsible AI and security as non-negotiables.

Key Responsibilities

• Conceptualize fast: embed with stakeholders, rapidly frame a solution to a business problem, and stand up a working prototype in days, not weeks.
• Own efficiency as the scorecard: drive measurable delivery efficiency and operational efficiency ; shorter cycle times, less manual effort, lower defect leakage, clear ROI.
• Engineer with AI leverage: use AI coding assistants / toolset across the SDLC to lift your own and the pod's productivity and quality.
• Apply agents and automation: work with coding agents, custom agents and reusable skills to automate delivery and operations workflows; build them where the problem warrants it.
• Get reliable AI output: apply prompt engineering and sound context practices (context engineering, prompt caching, RAG / context-graph patterns) so AI output is accurate, cost-aware and production-grade.
• Integrate to standards: design standards-based integrations using proven integration patterns that plug into client ecosystems predictably and securely.
• Make reusability and predictability the default: build assets, templates and patterns the pod and account can re-apply, so outcomes are consistent and repeatable.
• Prototype and iterate quickly: favor fast, testable prototypes over big up-front design; learn from each loop.
• Own DevOps and DevSecOps: CI/CD, shift-left security, infrastructure-as-code, and automated testing built in from day one.
• Run a continuous, adaptable feedback loop: use telemetry, quality signals, evals and client feedback to iterate both the solution and the AI behind it.
• Stay ahead of the curve: adopt emerging AI and engineering concepts quickly, and bring field learnings back to the practice.
• Lead and mentor: set technical direction for a lean team of 3 or 4, raise the engineering bar, and grow the pod's overall capability and AI fluency.

Skill Requirements

• Strong software engineering fundamentals - design, clean code, version control, testing, sound architecture; with hands-on full-stack delivery.
• Proven experience with standards-based integrations, integration patterns, DevOps/DevSecOps and test automation.
• Conceptual fluency in AI/GenAI and a working habit of using AI coding assistants for productivity; understands what agents, prompting, RAG and context graphs are and where they add value.
• Ability to conceptualize solutions to business problems quickly and operate effectively in ambiguous, customer-embedded settings.
• Client-facing maturity: translates fluidly between technical and non-technical stakeholders, and owns outcomes.
• Experience mentoring or leading small teams.
• A genuine appetite to deepen AI/GenAI skills fast.

What great looks like (strongly preferred)

• Hands-on experience building custom agents and reusable skills, not just consuming AI tools.
• Practical command of prompt engineering, context engineering, prompt caching and RAG / context-graph design, including cost and latency optimization.
• Experience designing agentic / GenAI solutions on platforms such as GHCP, Claude Code, Kiro, Cursor or comparable (multi-LLM, Responsible AI, governance-aware).
• A track record of turning field patterns into reusable accelerators adopted beyond a single engagement.

Other Requirements