Interested in this AI Product Manager role at Ensono?
Apply Now →About This Role
At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things*!* We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross\-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.
We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose.
Honesty – Reliability – Curiosity – Collaboration – Passion
About the role and what you’ll be doing:
Ensono’s Mainframe as a Service (MFaaS) suite is one of the most durable, mission\-critical managed service platforms in the market. As we incorporate AI capabilities and next\-generation platform innovations, this role exists to evolve and commercialize that portfolio — turning intelligence into offers that customers will buy.
This role is not responsible for defining core AI capabilities. Instead, it focuses on translating existing and emerging capabilities into productized, monetized services — including updating service definitions, pricing models, packaging, and positioning to align with market demand and client value.
You will operate at the intersection of product management, pricing strategy, and service commercialization, ensuring our managed services portfolio evolves from traditional, custom delivery to repeatable, scalable offerings with clear economic models grounded in an AI\-First operating model. You’ll set the roadmap, align the stakeholders, and own the outcomes.
Service Evolution \& Portfolio Refinement
- Adapt and evolve existing managed service offerings (e.g., MFaaS) to reflect new platform capabilities (e.g., Telum, Spyre) and AI\-enabled delivery improvements such as automation and productivity gains
- Update and maintain service descriptions, SOW constructs, and feature definitions
- Ensure offerings remain market\-relevant, clearly defined, and differentiated
- Keep every offering grounded in commercial reality: customer need, market timing, and Ensono’s right to win
Commercialization \& Pricing Strategy (Primary Focus)
- Co\-develop and refine costing and pricing models in partnership with Product Pricing and Operations
- Transition constructs toward standardized, scalable pricing approaches with forward\-looking economics
- Incorporate automation and AI\-driven efficiencies into cost\-to\-serve models
- Build and maintain pricing frameworks, SKU structures, and packaging tiers and bundles
- Ensure all offerings are profitable, repeatable, and defensible in the market
Product Packaging \& Monetization
- Productize services into clear, sellable offerings — core vs. add\-on capabilities, tiered service levels, and optional enhancements driven by AI or platform features
- Translate technical capabilities into client\-understandable value propositions and measurable business outcomes (cost savings, productivity, risk reduction)
- Develop standardized proposal constructs and pricing guidance for Sales
Portfolio Roadmap \& Lifecycle Ownership
- Own and maintain the commercialization roadmap across the full MFaaS portfolio
- Prioritize based on customer impact, commercial potential, and delivery feasibility
- Ensure every roadmap item has a clear, documented answer to “Why this? Why us? Why now?”
- Manage the full product lifecycle from ideation through commercial availability to retirement
Vendor \& Ecosystem Integration
- Assess and incorporate third\-party vendor capabilities into service offerings, evaluating fit for integration, differentiation, and margin impact
- Define how vendor technologies are packaged within managed services, priced and monetized, and positioned vs. alternatives (SaaS, ISVs, native tools)
- Manage product\-level relationships with key technology partners to ensure alignment with roadmap and commercial goals
- Ensure Ensono maintains a cohesive “wrapper” and value layer across vendor\-dependent services
Platform Feature Productization (Telum / Spyre / Next\-Gen Mainframe)
- Identify opportunities to package and monetize industry use cases using new platform\-level capabilities
- Translate hardware and software advancements (e.g., embedded AI processing) into new service features, differentiated offerings, and incremental revenue streams
- Work with Engineering and Architecture to ensure offerings are deployable, operationally supported, and ready for scale
Go\-to\-Market Enablement
- Develop and maintain product positioning, messaging, sales enablement materials, and offer definitions for pipeline and deal structuring
- Partner with Sales to move from point\-product or custom solutioning toward outcomes\-led selling of standardized offerings
- Support deal cycles with pricing guidance, packaging recommendations, and commercial clarity
Customer \& Market Insight
- Conduct ongoing customer discovery to validate assumptions, test positioning, and surface unmet needs
- Monitor the competitive landscape across managed services, AI\-enabled delivery, and platform alternatives
- Turn market signals into roadmap and packaging decisions — not just decks
Cross\-Functional Alignment
- Act as the bridge across Product \& Consulting lines of business, Pricing \& Finance, Delivery/Operations, and Sales
- Ensure alignment between what is sold, how it is priced, and how it is delivered
- Drive consistency across portfolio definitions and commercial models — and drive decisions rather than waiting for them
We want all new Associates to succeed in their roles at Ensono. That’s why we’ve outlined the job requirements below. To be considered for this role, it’s important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply.
Required Qualifications
- 7–10\+ years in Product Management or Service Portfolio Management, with clear ownership of commercial offer development in a managed services or technology services environment
- Managed services background — strong experience across mainframe, infrastructure, or SRE services, and an understanding of how managed service offers are structured, sold, and delivered
- Pricing \& commercialization expertise — a proven track record of productizing services (custom standardized) and developing or influencing costing and pricing models
- Commercial and financial acumen — cost\-to\-serve modeling, pricing strategy, and margin optimization
- Translation skill — you turn technical capability into clear packaging and sellable value propositions without losing either the technical or the business audience
- AI\-enabled delivery fluency — enough understanding of automation and AI efficiencies to reshape cost\-to\-serve and service design (this role applies AI to commercialization; it does not define the underlying AI capability)
- Cross\-organizational experience — you’ve worked across Product, Finance, and Delivery, and can drive alignment between them
- Clear communicator — you can run a cross\-functional meeting across pricing, finance, delivery, and sales and make sure everyone leaves knowing the decision and who owns what
- Ownership mindset — you drive the roadmap forward, manage dependencies, and don’t wait for blockers to resolve themselves
- Bachelor’s degree in Business, Computer Science, Engineering, or a related field — or equivalent demonstrated experience
Preferred Qualifications
- Mainframe ecosystem (IBM Z, z/OS, CICS, Db2\); IBM LinuxONE experience a plus
- Infrastructure Managed Services and/or SRE or Application Services
- Familiarity with AI\-enabled delivery (AIOps, automation, developer productivity)
- Vendor ecosystem familiarity (IBM, ISVs, observability, security, and DevOps tooling)
- Familiarity with next\-generation mainframe platform capabilities (e.g., Telum, Spyre, embedded AI processing)
Success Measures
- Adoption of standardized, productized service offerings
- Improved pricing consistency and margin performance
- Increased attach rates for add\-on and enhanced capabilities
- Conversion of platform innovations (e.g., Telum / Spyre) into revenue\-generating services
- Reduced reliance on custom pricing and one\-off deal structures
Role Differentiator
This role is not an AI product strategy role. Success is defined by the ability to take what exists — AI capabilities, platform features, and vendor tools — and turn it into clear, priced, packaged, and sellable services at scale.
Why Ensono?
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Ensono is a place to make better happen – for our clients and for your career. You can do great things through innovation or collaboration, by learning or volunteering, or to promote diversity and inclusion. You can do great things for your own health or for a healthier planet. Whatever it means to you to do great things we want Ensono to be the place you can do it.
We are a client\-facing business, but we do encourage clients to allow us to work remotely most of the time so if you are not required to be on a client site, you can choose to work from home or in our Ensono offices.
Some of our benefits include:
- Unlimited Paid Days Off
- Three health plan options
- 401k with company match
- Eligibility for dental, vision, short and long\-term disability, life and AD\&D coverage, and flexible spending accounts
- Family Forming Benefit including fertility coverage and adoption/surrogacy reimbursement
- Paid childbearing and paternal leave
- Education Reimbursement, Student Loan Assistance or 529 College Funding
- Sabbatical leave
- Wellness program
- Flexible work schedule
As of the date of this posting, a good faith estimate of the current pay scale for this role is $116,000 to 168,000 annually based on a full\-time schedule. Please note that placement in the range may vary based on numerous factors including but not limited to skills, experience, internal equity, and business needs. In addition to base salary, other compensation programs, depending on eligibility, include an annual bonus plan based on company and individual performanceand an equity grant under our Associate Equity Appreciation Program
Ensono is an Equal Opportunity/Affirmative Action employer. We are committed to providing equal employment to our Associates and building a diverse and inclusive workforce. All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or other legally protected basis, in accordance with applicable law.
Pay transparency nondiscrimination statement/posting OFCCP’s pay transparency policy can be found on OFCCP’s website. If you need accommodation at any point during the application or interview process, please let your recruiter know or email [email protected].
Salary Context
This $116K-$168K range is in the lower quartile for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Ensono, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 35% below the category median. Disclosed range: $116K to $168K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ensono AI Hiring
Ensono has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $168K - $168K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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