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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.
Role Overview
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We are seeking a highly experienced VP Software Engineering lead focusing on AI\-enabled engineering transformation, enterprise integration, and platform modernization within Transaction Banking Technology. The role combines hands\-on architecture leadership with delivery accountability across regulated banking platforms, including payments, workflow orchestration, real\-time integration, observability, and AI governance.
The VP will drive the design and production adoption of agentic GenAI capabilities, Graph RAG knowledge systems, and modernization accelerators that convert legacy platform complexity into clear specifications, reusable engineering patterns, and measurable delivery outcomes. This includes partnering closely with Product, Operations, SRE, QA, Security, Architecture, and vendor teams to ensure solutions are scalable, governed, auditable, and fit for enterprise production use.
A key priority is to strengthen internal engineering ownership by reducing long\-term dependence on third\-party platforms where appropriate, rebuilding critical capabilities within bank\-controlled platforms, and using AI\-assisted development to accelerate re\-platforming, operational diagnosis, and production support.
Key Responsibilities
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- Lead enterprise platform modernization programs across Transaction Banking with accountability for architecture, execution, adoption, and operational readiness.
- Design and productionize agentic GenAI solutions for engineering productivity, legacy modernization, operational diagnosis, and knowledge retrieval in regulated banking environments.
- Build and govern reusable AI platform patterns including prompt templates, tool/function calling, context management, reducer/summarization logic, evaluation instrumentation, and LLM gateway integration.
- Deliver Graph RAG and knowledge systems using graph databases, vector retrieval, text\-to\-query patterns, embeddings, and auditable execution ledgers.
- Own distributed integration architecture across REST APIs, IBM MQ, Kafka, file\-based interfaces, and event\-driven systems.
- Partner with enterprise governance, cyber, risk, and architecture teams to ensure AI, data, and integration solutions meet security, audit, resilience, and control expectations.
- Lead vendor architecture reviews, RFP evaluations, technical due diligence, and re\-platforming assessments to determine where capabilities should be bought, integrated, or insourced.
- Mentor senior engineers, establish reusable engineering frameworks, and promote adoption across US and global delivery teams.
Required Qualifications
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- 10\+ years of software engineering experience in financial services, with significant ownership of enterprise platforms, distributed systems, or banking technology delivery.
- Deep hands\-on experience with Java and Python, including microservices, REST APIs, SQL, integration frameworks, and production\-grade platform design.
- Strong experience with Generative AI or applied AI engineering, including LLM integration, LangChain/LangGraph or comparable orchestration frameworks, prompt engineering, RAG, agentic workflows, and evaluation approaches.
- Experience building governed, auditable systems in regulated environments, including metadata capture, run identifiers, token/cost accounting, traceability, failure classification, and operational controls.
- Strong cloud/platform engineering background including Kubernetes, Docker, CI/CD, health\-check frameworks, reliability engineering, and production observability.
- Demonstrated leadership across architecture, delivery, mentoring, cross\-team adoption, documentation, SRE partnership, QA strategy, and production support.
- Bachelor’s degree in Computer Science, Engineering, or related discipline; equivalent practical experience in enterprise software engineering will be considered.
Preferred Domain Expertise
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- Transaction Banking and payments technology including ACH, wires, payment initiation, clearing, settlement, reporting, exception handling, and reconciliation.
- SWIFT, ISO 20022, MT formats, BAI/BAI2, lockbox, account reporting, and bank file\-based integration patterns.
- Legacy modernization programs involving code analysis, specification generation, re\-platforming, vendor capability replacement, and measurable productivity improvement.
Leadership Expectations
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- Operate as a senior engineering leader who can translate ambiguous business and technology goals into executable architecture, delivery plans, and measurable outcomes.
- Communicate effectively with executive stakeholders, architects, product owners, risk partners, vendor teams, and engineering squads.
- Balance hands\-on technical credibility with people leadership, coaching, prioritization, delivery governance, and long\-term platform strategy.
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: $140k – $205k
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.
Salary Context
This $140K-$205K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At MUFG, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. This role's midpoint ($172K) sits 21% below the category median. Disclosed range: $140K to $205K.
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.
MUFG AI Hiring
MUFG has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Jersey City, NJ, US. Compensation range: $205K - $205K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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 Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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
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