Interested in this AI Product Manager role at Bank of America?
Apply Now →Skills & Technologies
About This Role
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
Bank of America is committed to an in\-office culture with specific requirements for office\-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role\-specific considerations.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
This job is responsible for maximizing the value for one or more products. Key responsibilities include working with stakeholders to understand their needs, and with product owners or specialists to ensure they are aligning on priorities. Job expectations include ensuring delivery of products that meet client expectations, including features, security, availability, resiliency, timelines and costs.
Position Summary:
This role is responsible for maximizing the value for one or more products, at the program level in a scaled Agile model. Key responsibilities include defining the vision and roadmap for their products, defining and prioritizing the features/epics in the program backlog, and articulating this to both stakeholders and development teams. This role works closely with stakeholders to understand their needs, and with Products Owners to ensure they are aligned on priorities. They support and collaborate with the Portfolio Product Executive on financials throughout delivery. They are experts of the products, business or technology domain, and the industry in which the product competes.
Responsibilities:
- Generates the vision and roadmap for the program based on customer requirements, industry trends, regulations, and the strategic direction for the business or technology domain
- Communicates the program vision and roadmap to stakeholders, product owners and the teams
- Oversees the budget(s) for one or more products by working closely with finance partners on both personnel and non\-personnel expenses
- Provides input into defining business capabilities and aligning them to products or creating new products to support them
- Ensures product availability by partnering closely with demand and capacity teammates
- Develops business cases for new products and additional features for existing products
- Partners with the product owners and the teams to ensure that optimum value is obtained through technology and subject\-matter expertise and understanding of the business and industry trends
- Define and execute a multi\-year product strategy for AI model enablement and Generative AI capabilities.
- Partner with executive leadership to identify high\-value AI use cases and align investments with business priorities.
- Establish roadmaps for AI platforms, model services, and reusable GenAI capabilities.
- As an expert, create the vision and roadmap for the program based on customer requirements, industry trends, regulations, and the strategic direction for the business or technology domain.
- Communicate the program vision and roadmap to stakeholders, Product Owners and the teams.
- Oversee the budget(s) for one or more products.
- Provide input into defining business capabilities and aligning them to products or creating new products to support them.
- Articulate the business value to Product Owners so they understand product and strategy; Ensure alignment across product roadmap
Required Qualifications:
- 7\+ years of Product Management experience with technology platforms, AI products, machine learning systems, or data\-driven platforms.
- Strong understanding of AI/ML concepts and model lifecycles.
- Deep knowledge of Large Language Models (LLMs) and Generative AI technologies.
- Experience working with OpenAI APIs and services or similar AI platforms.
- Understanding of Foundation models, Agentic AI frameworks, and Fine\-tuning methodologies
- Experience leading complex products from concept through production deployment.
- Ability to translate highly technical AI concepts into clear business outcomes.
- Excellent executive communication and stakeholder management skills.
- Experience within banking, financial services, fintech, or highly regulated industries.
Desired Qualifications:
- Knowledge of Model Risk Management (MRM) frameworks and AI governance practices.
- Experience with cloud\-based AI services (Azure AI, AWS Bedrock, Google Vertex AI).
- Familiarity with MLOps, LLMOps, and AI platform engineering.
- Experience launching AI copilots, conversational AI platforms, agentic workflows, or enterprise knowledge assistants.
- Understanding of emerging open\-source AI ecosystems and model deployment strategies.
Skills:
- Financial Management
- Influence
- Solution Delivery Process
- Stakeholder Management
- Technical Strategy Development
- Agile Practices
- Analytical Thinking
- Collaboration
- Result Orientation
- Risk Management
- Amazon Web Services (AWS)
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $135K-$217K range is below the median for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Bank of America, 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 Required
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 $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $135K to $217K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bank of America AI Hiring
Bank of America has 8 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Positions span Plano, TX, US, New York, NY, US, Pennington, NJ, US. Compensation range: $200K - $232K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.