Principal AI-First Product Manager

Job description

BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. Build the product system that turns deep mainframe expertise into trusted, governed AI experiences at enterprise scale. About the Role We are looking for a Principal AI-First Product Manager to join the BMC AMI Platform team. You will define and drive the product strategy for shared AI and agentic capabilities that enable AMI product teams to deliver intelligent, connected workflows across mainframe operations, data, development, and security. This is a senior individual-contributor role for a product leader who can move fluently between customer problems, AI technology, platform strategy, commercial outcomes, and delivery execution. You will turn emerging capabilities in generative AI and agentic systems into differentiated, trustworthy products that customers can adopt in mission-critical environments. What You Will Own AI-first product vision, strategy, and roadmap for a major AMI Platform area, spanning agentic orchestration, common intelligence services, governance, observability, knowledge, integrations, and user experiences. A portfolio of customer problems and use cases, prioritized by customer value, strategic differentiation, feasibility, reusable platform leverage, and business impact. End-to-end product outcomes from discovery and validation through build, launch, adoption, measurement, and iteration. The product definition of safe, observable, accountable autonomy, including human oversight, policy controls, auditability, evaluation, and operational readiness. Commercialization and adoption plans, including packaging inputs, pricing hypotheses, business cases, enablement, design-partner programs, and success metrics. Key Responsibilities Define a clear product strategy and roadmap that connects AMI Platform investments to customer outcomes and portfolio growth. Lead customer discovery with mainframe leaders, practitioners, and enterprise AI stakeholders; convert insights into validated problems, requirements, and differentiated product bets. Shape AI-native and agentic experiences, including assistants, specialized agents, coordinated workflows, tool use, knowledge grounding, and cross-domain orchestration. Partner closely with Engineering, Architecture, UX, AI Engineering, Data Science, Quality and Evaluation, Security, SRE, and portfolio product teams to make high-quality tradeoffs and deliver scalable platform capabilities. Define measurable product success across user value, agent and task success, quality, trust, adoption, retention, latency, reliability, cost, and business impact. Set product requirements for evaluation, guardrails, governance, RBAC, audit, observability, explainability, and human-in-the-loop controls appropriate for enterprise and regulated environments. Drive 0-to-1 and MVP development with disciplined hypotheses, prototypes, design partners, and evidence-based decisions, then scale successful patterns across products and customers. Balance common platform capabilities with domain-specific needs, avoiding one-off solutions while creating practical paths to customer value. Develop business cases, competitive analysis, packaging recommendations, launch plans, field enablement, and adoption programs for new AI capabilities. Represent the product strategy with executives, customers, analysts, partners, Sales, and Customer Success, using clear and defensible narratives. Raise the bar for AI-first product management practices and mentor product managers through frameworks, coaching, and example. What “AI-First” Means in This Role Start with the outcome and redesign the workflow around what AI can understand, recommend, coordinate, and safely execute, rather than adding a chatbot to an existing experience. Treat context, data, knowledge, tools, evaluation, observability, governance, and human oversight as core product capabilities, not implementation details. Use AI directly in the product-management workflow for research synthesis, rapid prototyping, scenario exploration, requirements quality, and ongoing product learning. Design for augmentation of human expertise, making expert knowledge more accessible and scalable while retaining accountability for consequential decisions. Must-Have Skills & Experience Extensive product-management experience in enterprise software, with evidence of setting strategy and delivering complex products across multiple teams or product areas. Demonstrated ownership of AI, generative AI, agentic AI, intelligent automation, AIOps, developer platforms, data platforms, or similarly complex technical products. Strong working knowledge of LLM-based systems, RAG and knowledge grounding, agents and tool use, orchestration, evaluation, observability, safety, and AI cost and performance tradeoffs. Understanding of how data quality, permissions, metadata, telemetry, and knowledge architecture affect AI product outcomes. Track record of translating ambiguous customer problems and emerging technologies into product strategy, roadmaps, requirements, and measurable outcomes. Experience taking 0-to-1 products or major capabilities from discovery through launch, adoption, and iteration. Excellent customer discovery, analytical, prioritization, and executive communication skills. Ability to influence Engineering, Architecture, UX, Security, GTM, and senior leaders without relying on formal authority. Strong commercial judgment, including business cases, market and competitive analysis, packaging, pricing inputs, and launch and adoption planning. Comfort operating in mission-critical enterprise environments where trust, security, deployment flexibility, and operational rigor matter. Bachelor’s degree or equivalent practical experience in a relevant field. Nice-to-Have Skills Experience with mainframe operations, observability, automation, database and data management, security, DevX, or enterprise infrastructure. Experience defining platform APIs, integration ecosystems, MCP-based capabilities, or extensible developer platforms. Knowledge of hybrid, on-premises, OpenShift, and cloud deployment models, including constraints in regulated or air-gapped environments. Experience with enterprise AI governance, responsible AI, model risk, compliance, or human-in-the-loop product design. Hands-on ability to prototype AI workflows using low-code tools, prompt and agent frameworks, APIs, or lightweight scripting. Experience working with design partners, analysts, strategic customers, and global go-to-market teams. Evidence We Will Look for in Hiring A product strategy or platform direction you personally shaped across more than one team or product. An AI or automation capability you took from an ambiguous problem to production adoption, with clear evidence of customer or business impact. A difficult tradeoff you made among customer value, technical feasibility, trust and risk, time-to-market, and platform scale. A measurement framework you established for AI quality and product success, including what changed because of the evidence. Examples of defining or improving the data, knowledge, or telemetry strategy behind an AI product, including how those choices affected relevance, accuracy, permissions, evaluation, or user trust. Examples of influencing senior technical and business stakeholders and simplifying a complex product story without losing accuracy. Product artifacts that show structured thinking: strategy, roadmap, discovery evidence, PRDs, prototypes, launch plans, or adoption reviews. At BMC, we don’t do ordinary. Our AI teams are building the next generation of agentic AI to help enterprises run their