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About This Role
Basic Information
Job Name Principal Product Manager - USA (B) Country United States State NA Date Published 05-Oct-2026 Job ID 47636 Travel You may occasionally be required to travel for business Additional Locations Detroit - Michigan, Houston - Texas, San Francisco - California, New York - New York, Washington - DC This role can be based remotely in United States Looking for more details about our benefits?
Description and Requirements
CareerArc Code CA-MH #LI-MH1 Hybrid: #LI-Hybrid 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 most critical systems faster, more resiliently, and with confidence. You will solve hard problems, shape smart products, and work with people who challenge and champion you, while seeing your work make a difference for customers operating mission-critical environments. #LI-Remote Our commitment to you!
BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!
If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!
BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.
BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process.
The annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs.
The range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits.
At BMC, it is not typical for an individual to be hired at /near the top of the range. A reasonable estimate of the current range is $175,800 - $293,000
Min salary 175,800 Mid point salary 234,400 Max salary 293,000 Min Salary - NEW 175,800 Max Salary - NEW 293,000
Salary Context
This $175K-$293K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BMC Software, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($234K) sits 11% above the category median. Disclosed range: $175K to $293K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BMC Software AI Hiring
BMC Software has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Santa Clara, CA, US, New York, NY, US. Compensation range: $254K - $329K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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