Interested in this AI/ML Engineer role at MUFG?
Apply Now →Skills & Technologies
About This Role
Do you want your voice heard and your actions to count?
Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long\-term relationships, serving society, and fostering shared and sustainable growth for a better world.
With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.
Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.
The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details.
Director \- Data Product \& AI Enablement Manager
Job Summary
The Data Product \& AI Enablement Manager is responsible for defining, building, and managing enterprise data products and AI\-enabled capabilities that drive business outcomes, operational efficiency, regulatory compliance, and innovation. This role serves as the bridge between business stakeholders, technology teams, data engineering, analytics, and AI specialists to ensure data and AI investments deliver measurable value.
The position will establish product strategy, prioritize roadmaps, oversee delivery, and drive adoption of data and AI solutions across the organization. The incumbent will lead cross\-functional teams and collaborate with business leaders to identify opportunities where data products, machine learning, generative AI, and automation can improve decision\-making, customer experience, risk management, and productivity.
Key Responsibilities
Data Product Strategy \& Management
- Develop and execute the vision, strategy, and roadmap for enterprise data products.
- Define product objectives, success metrics, and value realization plans.
- Partner with business stakeholders to understand requirements and prioritize product enhancements.
- Create and manage data product backlogs and delivery roadmaps.
- Establish governance, ownership, and lifecycle management for data products.
AI Strategy \& Enablement
- Identify and evaluate opportunities to leverage AI, machine learning, and generative AI capabilities.
- Lead the design and implementation of AI\-enabled business solutions.
- Partner with technology and risk teams to ensure responsible AI practices are embedded into all solutions.
- Drive adoption of AI platforms, copilots, intelligent automation, and agentic AI capabilities.
- Monitor AI performance, value realization, and business impact.
Product Delivery
- Work closely with engineering, architecture, analytics, and business teams to deliver scalable solutions.
- Lead Agile product management practices including roadmap planning, backlog prioritization, sprint reviews, and stakeholder engagement.
- Manage dependencies across multiple teams and initiatives.
- Ensure solutions meet quality, security, data governance, and regulatory requirements.
Stakeholder Management
- Serve as the primary liaison between business leaders and delivery teams.
- Communicate product strategy, progress, risks, and outcomes to executive stakeholders.
- Build strong partnerships across Technology, Operations, Risk, Compliance, Human Resources, Finance, and Business Units.
Data Governance \& Risk Management
- Promote data quality, lineage, governance, privacy, and security standards.
- Ensure compliance with regulatory expectations related to data and AI solutions.
- Support model governance, AI controls, and responsible AI frameworks.
People Leadership
- Lead, mentor, and develop a team of product managers, analysts, and data professionals.
- Foster a culture of innovation, experimentation, and continuous improvement.
- Build organizational capabilities in product management, data literacy, and AI adoption.
Qualifications
Education
- Bachelor's degree in Computer Science, Data Science, Finance, Information Systems, Engineering, Business, or related field.
- Master's degree preferred.
Experience
- 8–12\+ years of experience in product management, data management, analytics, technology, or related disciplines.
- 3–5\+ years leading cross\-functional teams or direct reports.
- Experience delivering enterprise data products or AI\-driven solutions.
- Experience operating within complex, regulated environments preferred.
- Demonstrated success driving business transformation through data and technology.
Technical Skills
- Strong understanding of:
+ Data products and data governance
+ Data architecture and cloud platforms
+ AI/ML and Generative AI technologies
+ Data visualization and analytics
+ Agile methodologies and product management practices
- Familiarity with:
+ Snowflake
+ Databricks
+ Azure AI
+ Microsoft Fabric
+ Microsoft Copilot
+ Large Language Models (LLMs)
+ Data marketplaces and APIs
Leadership Competencies
- Strategic thinking and business acumen.
- Ability to influence senior executives and drive decision\-making.
- Strong communication and stakeholder management skills.
- Results\-oriented mindset with a focus on delivering measurable business value.
- Ability to translate complex technical concepts into business outcomes.
Success Measures
The Data Product \& AI Manager will be evaluated on:
- Data product adoption and utilization
- Delivery of roadmap commitments
- AI use case implementation and value realization
- Productivity improvements achieved through data and AI solutions
- Data quality and governance outcomes
- Stakeholder satisfaction
- Team engagement and capability development
Education:
- Bachelor's degree in Computer Science or a closely\-related discipline, or an equivalent combination of formal education and experience
“ Visa sponsorship/support is based on business needs. We do not anticipate providing visa sponsorship/support for this position.”
The typical base pay range for this role is as follows:
- New York / New Jersey: $181\-234K
- Non–New York / New Jersey: $176\-215K
depending on job\-related knowledge, skills, experience and location. This role may also be eligible for certain discretionary performance\-based bonus and/or incentive compensation. Additionally, our Total Rewards program provides colleagues with a competitive benefits package (in accordance with the eligibility requirements and respective terms of each) that includes comprehensive health and wellness benefits, retirement plans, educational assistance and training programs, income replacement for qualified employees with disabilities, paid maternity and parental bonding leave, and paid vacation, sick days, and holidays. For more information on our Total Rewards package,
Our hybrid work schedule is four days on\-site and work remotely one day per week.
MUFG Benefits Summary
We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws (including (i) the San Francisco Fair Chance Ordinance, (ii) the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, (iii) the Los Angeles County Fair Chance Ordinance, and (iv) the California Fair Chance Act) to the extent that (a) an applicant is not subject to a statutory disqualification pursuant to Section 3(a)(39\) of the Securities and Exchange Act of 1934 or Section 8a(2\) or 8a(3\) of the Commodity Exchange Act, and (b) they do not conflict with the background screening requirements of the Financial Industry Regulatory Authority (FINRA) and the National Futures Association (NFA). The major responsibilities listed above are the material job duties of this role for which the Company reasonably believes that criminal history may have a direct, adverse and negative relationship potentially resulting in the withdrawal of conditional offer of employment, if any.
The above statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities duties and skills required of personnel so classified.
We are proud to be an Equal Opportunity Employer and committed to leveraging the diverse backgrounds, perspectives and experience of our workforce to create opportunities for our colleagues and our business. We do not discriminate on the basis of race, color, national origin, religion, gender expression, gender identity, sex, age, ancestry, marital status, protected veteran and military status, disability, medical condition, sexual orientation, genetic information, or any other status of an individual or that individual’s associates or relatives that is protected under applicable federal, state, or local law.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MUFG, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
MUFG AI Hiring
MUFG has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Jersey City, NJ, US, Walnut Creek, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.