AI Product Engineer

Role Summary

This role provides technical and delivery oversight across the IAIS (Industry AI Solutions) portfolio of AI / GenAI / Agentic AI solutions. The Product Engineering Lead assesses, solution by solution, whether each is being built as per the roadmap, at the expected quality, architecture and readiness, and then carries the best practices, reusable patterns and lessons from one solution to the others.

The role sits at the intersection of product, engineering and delivery. It works with IAIS Product Managers on what the roadmap commits to, with solution delivery teams and architects on how it is being built, and with program governance on how progress is reported. When a delivery team hits an operational or technical problem, this role brings the right expertise and the proven fix, so the team keeps moving.

Key Responsibilities

Portfolio Review & Roadmap Conformance

• Review multiple IAIS solutions concurrently through a structured review cadence (design, build-progress, release-readiness and post-release reviews), judging each against its roadmap commitments, scope, timelines and quality bar.
• Detect drift early: identify slippage, scope creep, architectural deviation, dependency gaps and technical debt, and agree corrective actions with the Product Manager and delivery lead.
• Track portfolio health through a consistent set of delivery and engineering KPIs (roadmap adherence, release predictability, quality and defect trends, incident and cost trends) and publish a portfolio view for program governance and senior stakeholders.

Technical Assurance

• Assess solution architecture and engineering practice for scalability, security, integration (including hyperscaler services), observability and maintainability.
• Apply AI / Agentic AI readiness checkpoints (evaluation metrics, guardrails and responsible-AI checks, quality gates, deployment and ops readiness) so that releases are repeatable and ship with confidence.
• Review operational risk: hallucination and quality issues, latency and token cost, data and integration failures, and production stability for solutions already live.

Best-Practice Translation & Enablement

• Identify what works in one solution (architecture patterns, evaluation approaches, prompt/agent design, LLMOps and CI/CD practices, accelerators) and translate it into reusable playbooks, reference architectures and standards for the other solutions.
• Spread learning across teams by running communities of practice, review forums and targeted coaching for delivery leads and engineers on GenAI / Agentic AI engineering and delivery.
• Help teams solve operational problems as and when they arise by pointing them to proven solutions, bringing in subject-matter experts, and capturing the resolution for the wider portfolio.

Stakeholder & Team Leadership

• Work with IAIS Product Managers and delivery leads to align roadmap, technical feasibility and delivery priorities, and to surface cross-solution dependencies and shared-platform needs.
• Guide and mentor the solution delivery leads and architects across the portfolio; set expectations on delivery discipline and engineering quality, and give feedback on their performance.
• Escalate clearly: raise risks and decisions with options and recommendations to program governance and senior leadership.

Qualifications

Must have

• BE/B.Tech (or equivalent) in Computer Science, Engineering or a related field; MBA / PG is a plus.
• 12+ years in IT services, product engineering or platform delivery, with a progression through developer, architect and delivery roles; has led or overseen more than one solution or team at a time.
• Strong technical grounding, enough to challenge architecture, code quality and engineering practice credibly: APIs and microservices, data pipelines, CI/CD, containers, observability and security.
• Working knowledge of at least one major hyperscaler's cloud and AI services and how they integrate with enterprise systems.
• Working knowledge of GenAI / Agentic AI solutions in production: LLM integration, RAG, agent orchestration, evaluation and guardrails, LLMOps.
• Experience running review and governance mechanisms (design reviews, stage gates, release-readiness checks) across several concurrent programs.
• Proven ability to codify and transfer best practices across teams, such as playbooks, standards and reference architectures.

Good to have

• Experience in a product-company or product-engineering environment, working with product managers on roadmaps and releases.
• Experience with LLMOps/MLOps, model-evaluation tooling and agent interoperability standards (e.g. MCP).
• Hyperscaler AI certification(s) and/or PMP / Scrum / SAFe certification.
• Exposure to responsible-AI, data privacy and compliance requirements for enterprise AI.

Expected Impact

• Solutions across the IAIS portfolio are built as per roadmap, with early detection of drift and fewer late surprises.
• Consistent engineering quality and release readiness across solutions, rather than strong in some and weak in others.
• Best practices and lessons learned travel quickly between solutions, reducing rework and raising team capability.
• Delivery teams resolve operational and technical problems faster because proven fixes and experts are readily available.
• Stakeholders have a single, credible, KPI-driven view of portfolio delivery health.

Skills

Solution Portfolio Review, Roadmap Conformance Tracking, Technical Assurance, GenAI / Agentic AI Architecture Understanding, Hyperscaler / Cloud Integration, Best-Practice Codification, Delivery Governance, Technical Problem Solving, Coaching & Mentoring, Product-Engineering Partnership, Risk/Issue Management, KPI & Metrics Reporting, Stakeholder Management