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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.
Director, Distinguished Engineer, Enterprise AI Platforms
Position Summary
MUFG Americas is building the foundational AI platforms, data and knowledge capabilities, governance patterns, and reusable engineering services needed to scale AI safely across the enterprise. The Director, Principal AI Platform Architect / Distinguished AI Engineer will be a senior hands\-on technical leader responsible for architecting and evolving enterprise AI platforms that enable business\-specific AI solutions to be built in a federated manner on common, governed, reusable foundations.
This role will lead core AI platform capabilities including LLM gateways, model\-provider abstraction, agent runtime, orchestration, RAG and knowledge integration, AI observability, evaluation frameworks, security guardrails, FinOps, and reusable engineering patterns. The successful candidate will combine deep engineering expertise, architectural judgment, product thinking, and strategic leadership to help MUFG move from early AI experimentation to governed, enterprise\-grade AI adoption.
This Director\-level technical leadership role requires the ability to set technical direction, influence senior stakeholders, mentor engineering teams, advise on build\-versus\-buy decisions, and translate emerging AI capabilities into reliable, secure, compliant, and reusable enterprise platforms.
Role Purpose
The purpose of this role is to build the enterprise AI foundation that allows MUFG to centralize hard\-to\-build, reusable, and control\-intensive capabilities while enabling business and technology teams to innovate faster at the edge.
The role will help ensure AI solutions are not built as isolated point solutions, but instead leverage shared services, trusted data, common governance controls, reusable components, and scalable engineering patterns. It supports MUFG’s layered AI strategy across common AI services, trusted data and knowledge, and AI Hub / marketplace capabilities for collaboration and reuse.
Key Responsibilities
1\. Enterprise AI Platform Architecture
- Lead the architecture and evolution of MUFG’s enterprise AI platform capabilities, including LLM gateways, model routing, provider abstraction, agent runtime, orchestration, prompt/context management, observability, and FinOps.
- Define reusable architecture patterns for AI applications, RAG pipelines, agentic workflows, AI\-assisted automation, model connectivity, and embedded AI services.
- Design platform services that support multiple model providers, cloud patterns, data sources, business domains, risk tiers, and AI consumption models, including tech\-built, citizen\-built, vendor\-enabled, and embedded AI solutions.
- Partner with Enterprise Architecture, Security, Infrastructure, Data Architecture, AI Governance, and application teams to ensure platforms are secure, scalable, resilient, auditable, and aligned with enterprise standards.
2\. Engineering Leadership and Hands\-on Delivery
- Serve as a hands\-on technical leader who reviews architecture, guides engineering decisions, develops prototypes, and helps teams solve complex design and implementation challenges.
- Lead development of reusable AI platform components, including model gateways, agent frameworks, tool registries, prompt libraries, evaluation pipelines, data connectors, orchestration patterns, and SDKs/APIs.
- Establish production\-grade engineering patterns for resilience, observability, latency, rate limiting, failover, caching, tenant fairness, usage attribution, cost optimization, CI/CD, automated testing, and production readiness.
- Create implementation blueprints that enable engineering teams and approved business builders to “compose, not rebuild.”
3\. AI Governance by Design
- Embed governance, risk, security, privacy, monitoring, auditability, and human oversight into AI platform architecture from the start.
- Partner with AI Governance, Operational Risk, Model Risk, Compliance, Legal, Privacy, Cybersecurity, and Data Governance teams to translate policy expectations into practical platform controls.
- Define technical control patterns for access control, data classification, entitlement\-aware retrieval, model usage monitoring, prompt/output logging, content filtering, human\-in\-the\-loop workflows, exception handling, and audit trails.
- Support AI use\-case intake and routing by assessing technical feasibility, reusability, architecture fit, risk implications, and platform readiness.
4\. Data, Knowledge, and Context Engineering
- Architect AI solutions that use trusted enterprise data, metadata, documents, ontologies, context graphs, and knowledge layers to improve relevance, explainability, and business usefulness.
- Partner with Data Architecture and Data Governance teams to ensure AI solutions use high\-quality, governed, lineage\-aware, entitlement\-controlled data.
- Define patterns for RAG, hybrid search, semantic retrieval, vector stores, knowledge graphs, metadata enrichment, document intelligence, and structured/unstructured data integration.
- Shape how AI Ready Data, data products, metadata, and enterprise knowledge are exposed safely and consistently to AI applications and agents.
5\. Strategic Technical Direction
- Create and maintain architecture roadmaps for enterprise AI capabilities, including agentic AI, multi\-agent orchestration, enterprise knowledge graphs, evaluation at scale, self\-service developer tooling, and platform interoperability.
- Advise senior technology and business leaders on build\-versus\-buy decisions, balancing speed, cost, control, reuse, vendor risk, and long\-term enterprise economics.
- Evaluate emerging AI technologies, including LLM providers, open\-weight models, AI frameworks, agent orchestration tools, evaluation platforms, observability products, vector databases, and AI security solutions.
- Establish technical principles, reference architectures, design standards, and reusable patterns that reduce fragmented experimentation and promote governed enterprise adoption.
6\. Cross\-functional Influence and Enablement
- Partner with business AI leads, product owners, engineers, data teams, architects, risk partners, and platform teams to convert business demand into scalable AI capabilities.
- Mentor senior engineers, architects, and solution teams on AI platform design, GenAI engineering patterns, and enterprise\-grade production practices.
- Communicate complex AI architecture concepts clearly to executives, business sponsors, risk partners, and technical teams.
- Act as a senior technical voice in architecture forums, AI governance forums, platform prioritization discussions, and strategic vendor evaluations.
Required Experience
- 15\+ years of experience in enterprise software engineering, platform engineering, architecture, data platforms, distributed systems, or related technology leadership roles.
- Hands\-on experience architecting and delivering production\-grade AI, GenAI, LLM, data, or enterprise platform capabilities.
- Experience designing enterprise AI platform services such as LLM gateways, model routing, provider abstraction, RAG services, agentic workflows, AI observability, model evaluation, prompt/context management, and AI guardrails.
- Strong background in cloud\-native architecture, APIs, microservices, event\-driven systems, containerization, CI/CD, infrastructure automation, and production operations.
- Deep understanding of enterprise data architecture, data governance, metadata, lineage, structured/unstructured data integration, data quality, and access\-control patterns.
- Experience with security, resilience, monitoring, auditability, cost optimization, and operational controls in regulated environments.
- Proven ability to influence architecture and engineering direction across multiple teams, with strong executive communication skills.
Preferred Technical Skills
- AI platform and LLMOps experience, including model gateways, provider abstraction, prompt management, observability, evaluation, guardrails, agent runtime, and AI cost management.
- Familiarity with GenAI frameworks and tools such as LangChain, LangGraph, CrewAI, LiteLLM, Ragas, Hugging Face, PyTorch, TensorFlow, SageMaker, or comparable platforms.
- Experience with model providers such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google/Gemini, open\-weight models, and enterprise integration patterns.
- Experience with data and knowledge platforms such as Snowflake, Databricks, Spark, Kafka, Airflow, vector search, OpenSearch/Elasticsearch, knowledge graphs, metadata platforms, and document intelligence.
- Strong engineering experience with Java, Python, Scala, JavaScript/TypeScript, SQL, Spring, FastAPI, Node.js, GraphQL, REST/OpenAPI, AWS, Kubernetes, Docker, Terraform, GitHub, and CI/CD.
- Understanding of governance and control patterns, including access controls, entitlements, data privacy, audit logging, model monitoring, AI inventory, model risk, secure\-by\-design reviews, and policy\-as\-code.
Education and Certifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field required.
“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: $ 250\-350K
- Non–New York / New Jersey: $ 250\-300K
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.
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. Director-level AI roles across all categories have a median of $274,554.
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
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