{"id":3606934,"url":"https://alion.io/job/hcltech-ai-product-manager-i-4","title":"AI Product Manager I","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Product","role_family":"Product","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI","optional":false}],"status":"live","first_seen_at":"2026-11-02T12:00:37Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-05T00:45:03Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\nThis role owns end-to-end execution of AI-driven product features and solution increments, bridging product definition and scaled delivery. It translates product vision into structured execution plans, aligned roadmaps, and measurable outcomes, while ensuring delivery predictability, cross-functional alignment, and readiness for scale. The role acts as a techno-functional anchor between product managers, engineering, and stakeholders, driving execution discipline, dependency management, and continuous improvement across AI / GenAI / Agentic AI solutions.\nKey Responsibilities\n· Translate product vision and business priorities into detailed execution plans, user stories, and prioritised backlogs for AI-driven features and solution components.\n· Drive end-to-end feature delivery ownership, including scope definition, sprint planning, dependency tracking, and milestone adherence.\n· Collaborate with engineering, architecture, and data teams to ensure technical feasibility, scalable design, and efficient iteration cycles.\n· Define and track execution metrics, quality benchmarks, and release readiness criteria (performance, reliability, safety, and adoption).\n· Manage cross-functional dependencies across engineering, GTM, alliances, and practice teams to maintain delivery cadence and minimise execution risks.\n· Embed Responsible AI practices through structured checkpoints, evaluation frameworks, and governance guardrails within delivery workflows.\n· Support adoption and scale by aligning product releases with GTM readiness, enablement, and stakeholder communication.\n· Leverage hyperscaler and partner ecosystems (Azure, AWS, Google Cloud, OpenAI, NVIDIA, etc.) to enable enterprise-ready AI deployments.\n· Monitor post-release performance and drive continuous improvements using data-driven insights and operational feedback loops.\nSkill Requirements\n· BE / BTech / MBA or equivalent with strong grounding in technology-led product execution and AI solution delivery.\n· 12 to 15 years of experience in product management, solution ownership, or techno-functional roles across IT services, platforms, or AI-driven products.\n· Hands-on experience delivering AI / GenAI / Agentic AI solutions across enterprise environments.\n· Exposure to hyperscaler ecosystems and AI service stacks, with understanding of scalable deployment and integration patterns.\n· Demonstrated ability to manage cross-functional execution, dependencies, and stakeholder alignment in complex delivery environments.\n· Strong understanding of product lifecycle from build to release to scale, including readiness and adoption considerations.\nOther Requirements\n·Drive predictable execution of AI product features through structured planning, milestone tracking, and disciplined delivery.\n·Improve delivery velocity and quality by proactively managing dependencies, risks, and execution bottlenecks.\n·Enable repeatable and scalable deployment patterns across AI solutions through standardised execution practices.\n·Strengthen adoption and business impact by aligning releases with GTM readiness and stakeholder needs.\n·Enhance visibility and control through metric-driven tracking of progress, quality, and 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