{"id":69996,"url":"https://alion.io/job/synechron-ai-product-program-management-manager","title":"AI Product & Program Manager – Generative AI, LLMs, Roadmap Strategy, Agile/SAFe & Governance","company":{"id":5306,"name":"Synechron","domain":"synechron.com","url":"https://alion.io/company/synechron","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":96,"open_postings":24,"ghost_share":0.042,"stale_share":0.167,"repost_share":0.167,"time_to_fill_p50_days":21,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Product","role_family":"Product","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":44000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":810},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scrum","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-08-22T00:00:00Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-09T04:40:01Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ghost","repost_count":3,"flags":["repost"],"days_open":78},"description":"Job Summary\nSynechron is seeking an AI Product / Program Management Manager with 10+ years of experience to lead the strategy, planning, delivery and governance of enterprise AI initiatives.The role will define AI product roadmaps, manage cross-functional programs, align business objectives with AI solutions and oversee execution from ideation through production. The position requires experience in product management, program delivery, stakeholder engagement, digital transformation and AI/Generative AI technologies.\nSoftware Requirements\nRequired\nProduct management and roadmap-management tools for defining:Product vision and strategyUse-case prioritiesProduct requirementsUser storiesSuccess metricsBusiness cases\nProgram and portfolio management tools for tracking:Program plansBudgetsResourcesTimelinesRisksIssuesDependenciesMilestones\nAgile delivery tools used to manage backlogs, sprints, releases, dependencies and delivery reporting.\nReporting and presentation tools used for executive-level status updates, governance forums, business cases and performance reporting.\nCollaboration and workshop tools used for requirements gathering, stakeholder alignment, executive reviews and cross-functional delivery.\nData analysis and dashboarding tools used to measure AI adoption, business impact, ROI, operational efficiency and customer outcomes.\nWorking knowledge of AI platforms and tools, including one or more of the following:Azure AIAzure OpenAIAWS AI/MLGoogle Vertex AIDatabricksSimilar enterprise AI platforms\nTools and frameworks used to support AI governance, privacy, security, risk management, compliance and model lifecycle oversight.\nPreferred\nAdvanced experience with product portfolio, investment and benefits-realization tools.\nExperience with tools supporting AI use-case intake, prioritization, model governance and responsible AI reviews.\nExperience with dashboarding and analytics tools for KPI tracking and executive reporting.\nExperience with vendor-management, procurement and contract-tracking tools.\nExperience with tools that support SAFe, Scrum, Agile planning and enterprise delivery governance.\nOverall Responsibilities\nProduct Strategy and Roadmap\nDefine and drive the vision, strategy and roadmap for AI and Generative AI products.\nIdentify business opportunities where AI can create measurable value, operational efficiency, improved customer experience or competitive advantage.\nWork with business leaders, clients, product teams and technology teams to assess, prioritize and sequence AI use cases.\nDevelop product requirements, user stories, success metrics, business cases and value hypotheses.\nAlign product roadmaps with organizational strategy, technology capabilities, data availability, regulatory expectations and delivery capacity.\nEstablish clear product outcomes and communicate priorities to all relevant stakeholders.\nProgram Management and Delivery\nLead end-to-end delivery of AI initiatives across multiple teams and stakeholder groups.\nManage program planning, budgeting, resource allocation, timelines, risks, issues, dependencies and delivery milestones.\nEstablish governance frameworks that support consistent decision-making, accountability and alignment with organizational objectives.\nTrack program progress and provide regular executive-level status updates.\nCoordinate delivery across product, engineering, data, architecture, security, compliance, operations and business teams.\nEnsure AI programs are delivered within agreed scope, budget and timelines, or that changes are formally assessed and communicated.\nDrive issue resolution, escalation management and corrective actions across the program lifecycle.\nAI and Technology Leadership\nCollaborate with Data Scientists, AI Engineers, Architects and Business Analysts to define AI-driven solutions.\nDrive the adoption of Generative AI, Machine Learning, NLP, Computer Vision and other relevant AI technologies.\nEvaluate AI platforms, tools and vendors against business needs, technical suitability, security, cost, scalability and support requirements.\nEnsure AI products are designed for scalability, reliability, security, maintainability and regulatory compliance.\nSupport decisions related to data readiness, model selection, model lifecycle, integration, deployment and operational support.\nTranslate technical risks and constraints into clear business impacts and delivery recommendations.\nStakeholder and Client Management\nAct as the primary interface between business stakeholders, clients and technical teams.\nFacilitate workshops, requirements-gathering sessions, use-case discovery sessions, prioritization forums and executive reviews.\nCommunicate program status, risks, issues, dependencies, decisions, outcomes and changes to senior leadership.\nBuild alignment across stakeholders with different priorities, levels of technical knowledge and business objectives.\nManage expectations, negotiate trade-offs and maintain clear communication throughout product and program delivery.\nCapture stakeholder feedback and ensure it is reflected in product decisions and delivery plans.\nGovernance and Risk Management\nImplement AI governance frameworks covering ethics, compliance, privacy, security, data usage and model risk.\nMonitor risks, issues and mitigation plans across AI programs.\nEnsure adherence to enterprise architecture, data governance and applicable regulatory standards.\nEstablish appropriate review points for AI use-case approval, solution design, model evaluation, release readiness and production monitoring.\nSupport responsible AI practices, including transparency, explainability, fairness, human oversight and appropriate controls.\nEnsure that production AI solutions have defined ownership, monitoring, support and escalation processes.\nPerformance and Value Realization\nDefine KPIs and success metrics for AI products and programs.\nMeasure business impact, ROI, adoption, customer outcomes and operational efficiency improvements.\nEstablish mechanisms to track benefits against approved business cases.\nUse data-driven insights to support continuous improvement and product decisions.\nIdentify opportunities to improve AI adoption, delivery efficiency, solution quality and business value.\nReport success metrics and value realization to relevant stakeholders and governance forums.\nSustainability Considerations\nPromote responsible and sustainable AI delivery by considering infrastructure efficiency, model utilization, data reuse, operational maintainability and long-term platform costs.\nEncourage reusable AI capabilities and shared services to reduce duplicated development and unnecessary resource consumption.\nInclude appropriate environmental, operational and lifecycle considerations when evaluating AI platforms and solution options.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nNo specific programming language is mandatory for the role.\nAbility to understand AI solution designs, technical dependencies, integration approaches, data requirements and production constraints.\nAbility to work effectively with technical teams and assess the delivery implications of AI implementation choices.\nPreferred\nWorking knowledge of Python and its use in AI, machine learning or data-processing solutions.\nFamiliarity with programming concepts used in APIs, microservices, data pipelines and AI application integration.\nDatabases and Data Management\nEssential\nUnderstanding of data requirements for AI and Generative AI initiatives.\nAbility to assess data availability, quality, privacy, ownership, access and readiness.\nUnderstanding of data governance, data lineage, data security and enterprise integration.\nAbility to collaborate with data teams on data ingestion, preparation, storage and usage requirements.\nUnderstanding of data considerations for Machine Learning, NLP, Computer Vision and LLM applications.\nPreferred\nExperience with SQL and NoSQL databases.\nFamiliarity with vector databases, semantic search and knowledge-management solutions.\nExperience with large-scale data platforms and data-processing environments.\nExperience defining data KPIs and quality measures for AI programs.\nCloud Technologies\nEssential\nWorking knowledge of enterprise cloud AI platforms, including one or more of:Azure AIAzure OpenAIAWS AI/MLGoogle Vertex AIDatabricksSimilar cloud AI platforms\nUnderstanding of cloud scalability, availability, security, integration, monitoring and cost management.\nAbility to evaluate cloud AI platform options against business, technical, governance and operational requirements.\nPreferred\nExperience leading cloud-based AI transformation programs.\nExperience evaluating cloud service providers, platform capabilities, architecture options and vendor proposals.\nFamiliarity with cloud-native AI deployment and operating models.\nFrameworks and Libraries\nEssential\nStrong understanding of the AI/ML lifecycle.\nPractical understanding of Generative AI applications, LLMs and prompt engineering.\nUnderstanding of AI use cases involving Machine Learning, NLP and Computer Vision.\nAbility to assess AI solution architectures, model-development approaches, evaluation methods and operational requirements.\nUnderstanding of responsible AI, model governance and AI risk-management practices.\nPreferred\nFamiliarity with RAG, embeddings, vector databases, AI agents and LLM orchestration frameworks.\nFamiliarity with model evaluation, monitoring and lifecycle-management frameworks.\nExperience with enterprise AI assistants, conversational applications or intelligent automation solutions.\nDevelopment Tools and Methodologies\nEssential\nStrong knowledge of Agile, Scrum, SAFe and product development methodologies.\nProduct roadmap development and backlog prioritization.\nProgram and portfolio management.\nBusiness case development and benefits realization.\nResource planning, budgeting, dependency management and delivery governance.\nRisk, issue, change and escalation management.\nStakeholder workshops, executive reviews and cross-functional planning.\nVendor evaluation and management.\nKPI definition, performance tracking and executive reporting.\nPreferred\nExperience with product operating models and enterprise transformation frameworks.\nExperience with structured AI use-case intake, prioritization and investment governance.\nExperience managing multiple AI products or programs as part of a broader portfolio.\nFamiliarity with CI/CD, MLOps and production support processes for AI solutions.\nSecurity Protocols\nEssential\nUnderstanding of AI security, privacy, compliance and governance requirements.\nAbility to identify risks relating to data privacy, model access, sensitive information, AI outputs and third-party services.\nUnderstanding of responsible AI principles, including transparency, explainability, fairness, human oversight and accountability.\nAbility to ensure that AI products meet relevant enterprise architecture, data governance, security and regulatory standards.\nExperience monitoring risks, controls, mitigation plans and governance decisions across AI programs.\nPreferred\nExperience with AI security frameworks, model-risk management and compliance assessments.\nExperience delivering AI programs in regulated or data-sensitive environments.\nFamiliarity with controls for prompt security, unauthorized access, data leakage and inappropriate AI outputs.\nExperience Requirements\nAt least 10 years of experience in Product Management, Program Management, Digital Transformation, Technology Delivery or a related field.\nAt least 3 years of experience leading AI/ML or Generative AI initiatives.\nProven experience managing large-scale cross-functional programs involving business, product, technology, data, architecture and operational teams.\nExperience defining product strategies, roadmaps, requirements, user stories, success metrics and business cases.\nExperience managing program plans, budgets, resource allocation, timelines, risks, issues and dependencies.\nStrong understanding of 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