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 operational teams.
• Experience defining product strategies, roadmaps, requirements, user stories, success metrics and business cases.
• Experience managing program plans, budgets, resource allocation, timelines, risks, issues and dependencies.
• Strong understanding of AI/ML lifecycle, Generative AI applications, LLMs, prompt engineering and AI governance.
• Experience with Agile, Scrum, SAFe and product development methodologies.
• Experience communicating program status, risks, decisions and business outcomes to senior leadership.
• Experience with Azure AI, Azure OpenAI, AWS AI/ML, Google Vertex AI, Databricks or similar platforms.
• Experience in BFSI, Healthcare, Retail, Technology or Consulting domains.
• Experience managing vendors, technology partners or external delivery teams.
• Candidates may qualify through equivalent experience in AI transformation, digital product management, technology program delivery, enterprise architecture or data and analytics leadership, provided they demonstrate comparable outcomes and capabilities.

Day-to-Day Activities

• Review AI product roadmaps, use-case priorities, business cases, delivery plans, budgets, risks, dependencies and value-realization metrics.
• Collaborate with business leaders, product owners, clients, Data Scientists, AI Engineers, Architects, Business Analysts, DevOps, MLOps and security teams.
• Facilitate requirements workshops, prioritization meetings, architecture discussions, governance reviews, executive updates and delivery-status sessions.
• Make or facilitate product and program decisions within the agreed governance framework, escalate risks when required and remain accountable for delivery alignment, stakeholder communication and measurable outcomes.

Qualifications

• A bachelor’s or master’s degree in Engineering, Computer Science, Information Technology, Business Management or a related field is required or preferred based on applicable experience.
• An MBA or equivalent business qualification is an added advantage.
• Certifications such as PMP, PgMP, SAFe, PMI-ACP, CSPO or relevant AI certifications are preferred.
• Training in AI/ML lifecycle management, Generative AI, AI governance, responsible AI, Agile delivery and program management is preferred.
• Commitment to continuous professional development in AI product management, emerging AI technologies, cloud platforms, governance, digital transformation and value realization is expected.

Professional Competencies

• Applies structured critical thinking to assess AI opportunities, evaluate business cases, manage trade-offs and resolve complex product and program challenges.
• Provides effective leadership by aligning cross-functional teams, clarifying priorities, supporting accountability and enabling coordinated delivery.
• Communicates product strategy, program status, risks, decisions, technical considerations and business outcomes clearly to executive, business and technical stakeholders.
• Adapts to evolving AI capabilities, market conditions, regulatory expectations, delivery constraints and changing organizational priorities.
• Identifies practical opportunities to apply Generative AI, Machine Learning, automation and data-driven products to create measurable business value.
• Manages time, priorities, budgets, resources, dependencies and delivery commitments while maintaining focus on scope, quality, governance, adoption and ROI.

S​YNECHRON’S DIVERSITY & INCLUSION STATEMENT
 

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

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