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About This Role
JOB DESCRIPTION
Ignite your passion for product innovation by leading customer\-centric development, inspiring solutions, and shaping the future with your strategic vision and influence.
As a Product Director in the Consumer \& Community Banking Fraud Strategy organization, you will play a pivotal role in defining the future of fraud prevention through advanced machine learning and artificial intelligence. In this senior leadership position, you will own the product vision and multi\-year roadmap for a next\-generation, real\-time fraud intelligence platform that protects millions of customers across digital banking, payments, and financial ecosystems.
Leverage your expertise in transformer\-based ML, graph intelligence, continuous learning, and modern MLOps, you will build capabilities to detect fraud rings, coordinated attacks, and multi\-step fraud schemes. You will drive innovation in dynamic feature engineering, graph\-based risk detection, sequence modeling, and experimentation frameworks to rapidly deliver governed fraud models that protect customers. Partnering with Fraud Operations, Data Science, Engineering, Product, and Model Risk Management, you will deliver measurable fraud\-loss reduction while preserving an excellent customer experience. You will also communicate platform strategy, experiment outcomes, and capability roadmaps to senior leaders to inform decisions and continuously improve fraud prevention.
Job responsibilities* Oversees the product roadmap, vision, development, execution, risk management, and business growth targets
- Leads the entire product life cycle through planning, execution, and future development by continuously adapting, developing new products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth
- Coaches and mentors the product team on best practices, such as solution generation, market research, storyboarding, mind\-mapping, prototyping methods, product adoption strategies, and product delivery, enabling them to effectively deliver on objectives
- Owns product performance and is accountable for investing in enhancements to achieve business objectives
- Monitors market trends, conducts competitive analysis, and identifies opportunities for product differentiation
- Own the multi\-year platform strategy and roadmap for fraud models, dynamic feature infrastructure (incl. streaming \+ feature store), graph intelligence, and MLOps across CCB payment and banking products.
- Lead experimentation and delivery with clear success criteria/lift metrics, converting validated POCs into production capabilities that reduce fraud loss and improve customer experience.
- Productize graph intelligence for fraud rings (entity schema, graph features/embeddings, freshness/latency SLAs, and explainability requirements).
- Establish end\-to\-end model lifecycle standards (model CI/CD, evaluation gates, monitoring, drift detection, automated retraining, and rollback) to ensure safe, reliable deployment.
- Embed governance by design including explainability, bias/fairness checks, and Model Risk documentation to meet regulatory expectations.
- Build strong partnerships and team capability by developing a high\-performing product org, collaborating cross\-functionally (Product, Engineering, Data Science, Fraud Ops, MRM), staying ahead of industry trends, and translating technical topics for executives.
Required qualifications, capabilities, and skills* 8\+ years of experience or equivalent expertise delivering products, projects, or technology applications
- Extensive knowledge of the product development life cycle, technical design, and data analytics
- Proven ability to influence the adoption of key product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
- Experience driving change within organizations and managing stakeholders across multiple functions
- Bachelor's degree
- 5\+ years building or owning ML\-enabled products such as feature platforms, model platforms, or fraud decisioning systems in production environments.
- Deep expertise in fraud, payments risk, trust and safety, cybersecurity, or similarly adversarial domains where models face adaptive threats.
- Strong technical fluency across applied machine learning, data systems, and production constraints including latency, reliability, monitoring, and scale.
- Proven track record of leading cross\-functional execution with Product, Engineering, Data Science, Operations, and Model Risk Management teams.
- Exceptional communication and executive presence; comfortable translating technical capabilities into business value for senior leadership.
- Strong analytical skills and ability to define success metrics, evaluate experimentation results, and make data\-driven platform decisions.
Preferred qualifications, capabilities, and skills* Recognized thought leader within a related field
- Advanced degree in Computer Science, Machine Learning, Statistics, or related quantitative field
- Hands\-on experience building and scaling transformer\-based or other large\-scale sequence models in production, using high\-volume event data (e.g., fraud, security, behavioral analytics, risk).
- Experience productizing graph features/embeddings or graph ML for fraud ring detection, network analysis, or risk assessment.
- Proven RL system design in live environments (optimization/control/fraud decisioning), including reward design, online/offline evaluation, and safe deployment in adversarial settings.
- Experience designing feature stores and maintaining online/offline parity at scale for real\-time decisioning systems.
- Strong representation learning/embeddings and long\-horizon temporal modeling skills, familiarity with financial services model governance, and demonstrated thought leadership (papers, patents, talks, or open source).
ABOUT US
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission\-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on\-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer \& Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most\-used digital solutions – all while ranking first in customer satisfaction.
We offer a broad array of credit cards to meet the needs of individuals and small businesses, including Chase\-branded and co\-branded cards in partnership with well\-known companies and organizations. Merchant Services is a leading provider of payment, fraud and data security for companies, capable of authorizing transactions across global currencies.
Salary Context
This $180K-$285K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $187K across 23 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,133 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 91 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($232K) sits 7% above the category median. Disclosed range: $180K to $285K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 82 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, MLOps Engineer. Positions span Plano, TX, US, Brooklyn, NY, US, New York, NY, US. Compensation range: $115K - $325K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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