AI Product & Program Manager – Generative AI, LLMs, Roadmap Strategy, Agile/SAFe & Governance at Synechron in Bengaluru - Bellandur (GTP)

Job description

• 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.

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.
• 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.

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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Skills

Required

• Program and portfolio management tools
• Agile delivery tools
• Reporting and presentation tools
• Collaboration and workshop tools
• Data analysis and dashboarding tools
• Program and portfolio management
• Vendor evaluation and management

Preferred

• Experience with SQL and NoSQL databases