AI Product & Program Manager – Generative AI, LLMs, Roadmap Strategy, Agile/SAFe & Governance

Job Summary

Synechron 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.

Software Requirements

Required

• •Product management and roadmap-management tools for defining:Product vision and strategyUse-case prioritiesProduct requirementsUser storiesSuccess metricsBusiness cases
• •Program and portfolio management tools for tracking:Program plansBudgetsResourcesTimelinesRisksIssuesDependenciesMilestones
• •Agile delivery tools used to manage backlogs, sprints, releases, dependencies and delivery reporting.
• •Reporting and presentation tools used for executive-level status updates, governance forums, business cases and performance reporting.
• •Collaboration and workshop tools used for requirements gathering, stakeholder alignment, executive reviews and cross-functional delivery.
• •Data analysis and dashboarding tools used to measure AI adoption, business impact, ROI, operational efficiency and customer outcomes.
• •Working knowledge of AI platforms and tools, including one or more of the following:Azure AIAzure OpenAIAWS AI/MLGoogle Vertex AIDatabricksSimilar enterprise AI platforms
• •Tools and frameworks used to support AI governance, privacy, security, risk management, compliance and model lifecycle oversight.

Preferred

• •Advanced experience with product portfolio, investment and benefits-realization tools.
• •Experience with tools supporting AI use-case intake, prioritization, model governance and responsible AI reviews.
• •Experience with dashboarding and analytics tools for KPI tracking and executive reporting.
• •Experience with vendor-management, procurement and contract-tracking tools.
• •Experience with tools that support SAFe, Scrum, Agile planning and enterprise delivery governance.

Overall Responsibilities

Product Strategy and Roadmap

• •Define and drive the vision, strategy and roadmap for AI and Generative AI products.
• •Identify business opportunities where AI can create measurable value, operational efficiency, improved customer experience or competitive advantage.
• •Work with business leaders, clients, product teams and technology teams to assess, prioritize and sequence AI use cases.
• •Develop product requirements, user stories, success metrics, business cases and value hypotheses.
• •Align product roadmaps with organizational strategy, technology capabilities, data availability, regulatory expectations and delivery capacity.
• •Establish clear product outcomes and communicate priorities to all relevant stakeholders.

Program Management and Delivery

• •Lead end-to-end delivery of AI initiatives across multiple teams and stakeholder groups.
• •Manage program planning, budgeting, resource allocation, timelines, risks, issues, dependencies and delivery milestones.
• •Establish governance frameworks that support consistent decision-making, accountability and alignment with organizational objectives.
• •Track program progress and provide regular executive-level status updates.
• •Coordinate delivery across product, engineering, data, architecture, security, compliance, operations and business teams.
• •Ensure AI programs are delivered within agreed scope, budget and timelines, or that changes are formally assessed and communicated.
• •Drive issue resolution, escalation management and corrective actions across the program lifecycle.

AI and Technology Leadership

• •Collaborate with Data Scientists, AI Engineers, Architects and Business Analysts to define AI-driven solutions.
• •Drive the adoption of Generative AI, Machine Learning, NLP, Computer Vision and other relevant AI technologies.
• •Evaluate AI platforms, tools and vendors against business needs, technical suitability, security, cost, scalability and support requirements.
• •Ensure AI products are designed for scalability, reliability, security, maintainability and regulatory compliance.
• •Support decisions related to data readiness, model selection, model lifecycle, integration, deployment and operational support.
• •Translate technical risks and constraints into clear business impacts and delivery recommendations.

Stakeholder and Client Management

• •Act as the primary interface between business stakeholders, clients and technical teams.
• •Facilitate workshops, requirements-gathering sessions, use-case discovery sessions, prioritization forums and executive reviews.
• •Communicate program status, risks, issues, dependencies, decisions, outcomes and changes to senior leadership.
• •Build alignment across stakeholders with different priorities, levels of technical knowledge and business objectives.
• •Manage expectations, negotiate trade-offs and maintain clear communication throughout product and program delivery.
• •Capture stakeholder feedback and ensure it is reflected in product decisions and delivery plans.

Governance and Risk Management

• •Implement AI governance frameworks covering ethics, compliance, privacy, security, data usage and model risk.
• •Monitor risks, issues and mitigation plans across AI programs.
• •Ensure adherence to enterprise architecture, data governance and applicable regulatory standards.
• •Establish appropriate review points for AI use-case approval, solution design, model evaluation, release readiness and production monitoring.
• •Support responsible AI practices, including transparency, explainability, fairness, human oversight and appropriate controls.
• •Ensure that production AI solutions have defined ownership, monitoring, support and escalation processes.

Performance and Value Realization

• •Define KPIs and success metrics for AI products and programs.
• •Measure business impact, ROI, adoption, customer outcomes and operational efficiency improvements.
• •Establish mechanisms to track benefits against approved business cases.
• •Use data-driven insights to support continuous improvement and product decisions.
• •Identify opportunities to improve AI adoption, delivery efficiency, solution quality and business value.
• •Report success metrics and value realization to relevant stakeholders and governance forums.

Sustainability Considerations

• •Promote responsible and sustainable AI delivery by considering infrastructure efficiency, model utilization, data reuse, operational maintainability and long-term platform costs.
• •Encourage reusable AI capabilities and shared services to reduce duplicated development and unnecessary resource consumption.
• •Include appropriate environmental, operational and lifecycle considerations when evaluating AI platforms and solution options.

Technical Skills (By Category)

Programming Languages

Essential

• •No specific programming language is mandatory for the role.
• •Ability to understand AI solution designs, technical dependencies, integration approaches, data requirements and production constraints.
• •Ability to work effectively with technical teams and assess the delivery implications of AI implementation choices.

Preferred

• •Working knowledge of Python and its use in AI, machine learning or data-processing solutions.
• •Familiarity with programming concepts used in APIs, microservices, data pipelines and AI application integration.

Databases and Data Management

Essential

• •Understanding of data requirements for AI and Generative AI initiatives.
• •Ability to assess data availability, quality, privacy, ownership, access and readiness.
• •Understanding of data governance, data lineage, data security and enterprise integration.
• •Ability to collaborate with data teams on data ingestion, preparation, storage and usage requirements.
• •Understanding of data considerations for Machine Learning