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About This Role
Who we are looking for
We are looking for an Artificial Intelligence \& Machine Learning, Vice President with a robust background in Generative AI, Machine Learning, enterprise architecture, cloud platforms, and scalable solution design. You will be responsible for designing and implementing AI\-enabled solutions that are secure, resilient, compliant, and aligned with enterprise architecture standards. This role requires strong experience in translating business requirements into end\-to\-end AI solution architectures, including platform integration, governance, observability, and production readiness. The ideal candidate will work across business, engineering, security, data, and risk teams to deliver scalable and responsible AI solutions for enterprise use cases.
Why this role is important to us
The team you will be joining plays a vital role in enabling enterprise AI adoption by building secure, scalable, and governance\-aligned architecture for Generative AI and Machine Learning solutions. This role is important because it helps ensure AI solutions are designed for production readiness, compliance, interoperability, and long\-term business value across the organization.
What you will be responsible for
As an Artificial Intelligence \& Machine Learning, Vice President, you will:
- Design end\-to\-end AI/ML and Generative AI solution architectures for enterprise use cases, including model integration, orchestration, APIs, data flows, and downstream system interactions.
- Define and implement reference architectures, architecture standards, and best practices for AI platforms and shared services.
- Collaborate with business stakeholders, product teams, data scientists, and engineering teams to convert business problems into scalable technical solutions.
- Lead architecture design for multi\-cloud and hybrid AI environments, ensuring scalability, resiliency, security, and interoperability.
- Ensure solutions comply with Responsible AI, governance, data handling, testing, and risk management requirements.
- Drive POCs, architecture assessments, technical reviews, and production onboarding for AI applications and platforms.
- Partner with platform, cloud, security, and DevSecOps teams to ensure production readiness, observability, access control, and operational excellence.
- Establish reusable design patterns for prompt engineering, RAG, model serving, agentic workflows, and AI service integration where applicable.
- Support architecture governance forums and provide technical leadership on solution trade\-offs, standards, and modernization opportunities.
What we value
These skills will help you succeed in this role:
- Strong critical thinking, problem\-solving, and decision\-making skills in complex enterprise AI environments.
- Deep hands\-on expertise in Generative AI, LLMs, ML systems, RAG, vector databases, prompt design, and AI application integration.
- Strong knowledge of cloud platforms such as Azure, AWS, or GCP, including AI/ML services, storage, networking, and security.
- Expertise in designing scalable, secure, and resilient enterprise architectures for data\- and AI\-driven applications.
- Strong stakeholder management and cross\-functional collaboration skills, with the ability to communicate complex technical concepts clearly to both technical and non\-technical audiences.
Education \& Preferred Qualifications
- BS, B\-TECH, or Master's degree in Computer Science, Computer Information Systems, Engineering, or Mathematics.
- 10\+ years of experience in solution architecture, enterprise application architecture, or AI/ML platform architecture.
- Strong understanding of DevSecOps, CI/CD, monitoring, observability, and platform engineering concepts.
- Expertise in API architecture, microservices, distributed systems, system integration, and event\-driven architectures.
- Experience with AI governance, Responsible AI, model risk, compliance, testing, and production controls.
Additional requirements
- Experience in financial services or other highly regulated industries.
- Exposure to agentic AI frameworks, AI gateways, federated AI patterns, and enterprise AI shared services.
- Hands\-on knowledge of Databricks, Kubernetes, container platforms, and MLOps tooling.
- Familiarity with document intelligence, workflow orchestration, and intelligent automation platforms.
- Experience with architecture governance boards, security reviews, and control frameworks.
- Certifications in cloud architecture, AI/ML, or enterprise architecture are a plus.
- Experience mentoring engineering teams and driving architecture adoption across large organizations.
Work Requirement
- Hybrid work requirement: To be updated based on role/location requirement
- Shift timings: To be specified
Salary Range:
$120,000 \- $202,500 Annual
The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.
*Employees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long\-term disability, and other optional additional coverages; paid\-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance\-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.*
*For a full overview, visit* *https://hrportal.ehr.com/statestreet/Home**.*
About State Street
======================
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work\-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $120K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At State Street, 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 $185,000 based on 13,200 positions with disclosed compensation. This role's midpoint ($161K) sits 13% below the category median. Disclosed range: $120K to $202K.
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.
State Street AI Hiring
State Street has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Quincy, MA, US, Burlington, MA, US, Boston, MA, US. Compensation range: $91K - $217K.
Location Context
AI roles in Boston pay a median of $216,350 across 460 tracked positions. That's 8% above the national 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 4,133 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).
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 4,133 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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