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, NLP, Computer Vision and LLM applications.

Preferred

• Experience with SQL and NoSQL databases.
• Familiarity with vector databases, semantic search and knowledge-management solutions.
• Experience with large-scale data platforms and data-processing environments.
• Experience defining data KPIs and quality measures for AI programs.

Cloud Technologies

Essential

• Working knowledge of enterprise cloud AI platforms, including one or more of:Azure AIAzure OpenAIAWS AI/MLGoogle Vertex AIDatabricksSimilar cloud AI platforms
• Understanding of cloud scalability, availability, security, integration, monitoring and cost management.
• Ability to evaluate cloud AI platform options against business, technical, governance and operational requirements.

Preferred

• Experience leading cloud-based AI transformation programs.
• Experience evaluating cloud service providers, platform capabilities, architecture options and vendor proposals.
• Familiarity with cloud-native AI deployment and operating models.

Frameworks and Libraries

Essential

• Strong understanding of the AI/ML lifecycle.
• Practical understanding of Generative AI applications, LLMs and prompt engineering.
• Understanding of AI use cases involving Machine Learning, NLP and Computer Vision.
• Ability to assess AI solution architectures, model-development approaches, evaluation methods and operational requirements.
• Understanding of responsible AI, model governance and AI risk-management practices.

Preferred

• Familiarity with RAG, embeddings, vector databases, AI agents and LLM orchestration frameworks.
• Familiarity with model evaluation, monitoring and lifecycle-management frameworks.
• Experience with enterprise AI assistants, conversational applications or intelligent automation solutions.

Development Tools and Methodologies

Essential

• Strong knowledge of Agile, Scrum, SAFe and product development methodologies.
• Product roadmap development and backlog prioritization.
• Program and portfolio management.
• Business case development and benefits realization.
• Resource planning, budgeting, dependency management and delivery governance.
• Risk, issue, change and escalation management.
• Stakeholder workshops, executive reviews and cross-functional planning.
• Vendor evaluation and management.
• KPI definition, performance tracking and executive reporting.

Preferred

• Experience with product operating models and enterprise transformation frameworks.
• Experience with structured AI use-case intake, prioritization and investment governance.
• Experience managing multiple AI products or programs as part of a broader portfolio.
• Familiarity with CI/CD, MLOps and production support processes for AI solutions.

Security Protocols

Essential

• Understanding of AI security, privacy, compliance and governance requirements.
• Ability to identify risks relating to data privacy, model access, sensitive information, AI outputs and third-party services.
• Understanding of responsible AI principles, including transparency, explainability, fairness, human oversight and accountability.
• Ability to ensure that AI products meet relevant enterprise architecture, data governance, security and regulatory standards.
• Experience monitoring risks, controls, mitigation plans and governance decisions across AI programs.

Preferred

• Experience with AI security frameworks, model-risk management and compliance assessments.
• Experience delivering AI programs in regulated or data-sensitive environments.
• Familiarity with controls for prompt security, unauthorized access, data leakage and inappropriate AI outputs.

Experience Requirements

• At least 10 years of experience in Product Management, Program Management, Digital Transformation, Technology Delivery or a related field.
• At least 3 years of experience leading AI/ML or Generative AI initiatives.
• Proven experience managing large-scale cross-functional programs involving business, product, technology, data, architecture and oper