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About This Role
Technology Financial Operations AI & Automation Engineer
Who We Are Looking For
We are looking for a Technology Financial Operations AI & Automation Engineer reporting to the Technology Financial Operations Office. This role builds and supports the automations and AI-enabled workflows that cut manual effort, strengthen controls, and modernize recurring routines across technology cost, Cloud FinOps, TCO, productivity, portfolio, demand, procurement, headcount, and operational reporting.
Why This Role Is Important To Us
The role analyzes how work gets done today, builds the workflow automation and AI tooling to do it better, and supports it once it is live. The work is to replace manual follow-up, spreadsheet checks, and point solutions with governed automation that has clear ownership, reliable support, and measurable value.
This role requires comfort with both business users and technical partners, and the ability to turn a manual routine into automation.
The role uses approved enterprise platforms and responsible AI patterns, and is judged on what reaches production and gets used. This role does the building: solving each problem with emerging technology, applying common standards, and reusing the result across functions instead of rebuilding it for every process.
Near-term opportunities include intake and triage automation, procurement and TPRM knowledge assistance, PO/GL and accrual checks, timesheet and headcount agents, productivity initiative tracking, and support for IT TCO and FinOps tooling. Success looks like automations running in production, used by the teams they were built for, clearly owned and supported, and cutting manual effort without weakening controls.
What You Will Be Responsible For
- Identify and prioritize automation opportunities across Technology Financial Operations Office functions based on value, repeatability, risk, control needs, and maintainability.
- Partner with business SMEs to map current processes, simplify where appropriate, and translate requirements into workflow designs, control points, exception paths, and support needs.
- Build, deploy, and support automations using approved enterprise tools, including Microsoft Power Platform, workflow technologies, AI-enabled productivity tools, and related integration patterns.
- Use AI where cost returns value: intake triage, knowledge assistance, document and data review, exception identification, summarization, routing, and human-in-the-loop decision support.
- Support TCO, FinOps, productivity, portfolio, demand, procurement, headcount, and operational reporting routines by automating intake, validation, exception management, workflow routing, and recurring checks.
- Run automations as supported products, with documentation, monitoring, release discipline, user guidance, a clear route for reporting issues, and a plan for improving them over time.
- Work with data, BI, app ownership, architecture, security, risk, controls, audit, and responsible AI partners to ensure solutions are compliant, auditable, secure, and supportable.
- Track outcomes such as effort avoided, cycle-time reduction, error reduction, control improvement, adoption, and user satisfaction.
What We Value
These skills will help you succeed in this role
- Sound engineering habits, and the judgment to build something maintainable rather than over-engineered.
- The instinct to ask whether a process should be simplified or retired before it gets automated.
- A realistic view of what AI does well, what it does badly, and where it can and cannot run autonomously.
- Treating controls, testing, and auditability as part of the design rather than a step at the end.
- Clear communication style for non-technical SMEs, finance partners, technology teams, and senior stakeholders.
- Willingness to own what is built after launch, including releases, documentation, controls, and results.
Education and Preferred Qualifications
- Bachelor’s degree in Data Analytics, Information Systems, Computer Science, Finance, Business Analytics, Engineering, or a related field preferred; equivalent experience may be considered.
- Experience designing, building, and supporting workflow automations, low-code solutions, AI-enabled assistants, or similar enterprise productivity tools.
- Hands-on experience with Microsoft Power Platform, Power Automate, Copilot & Copilot Studio, and AI-enabled productivity tools, SharePoint, Power BI, or comparable enterprise automation technologies.
- Experience mapping business processes, documenting requirements, designing future-state workflows, and supporting adoption with non-technical stakeholders.
- Understanding data handling, access control, auditability, testing, change management, and support expectations in a governed enterprise environment.
- Strong analytical and prioritization skills, with a track record of choosing which opportunities to pursue and which to decline.
- Experience with enterprise AI or agentic workflow patterns, including retrieval-based assistants, knowledge bases, routing agents, review assistants, or workflow copilots.
- Experience with financial services, technology business management, FinOps, IT TCO, procurement, third-party risk, portfolio management, or technology finance operations.
- Familiarity with Microsoft Fabric, Dataverse, APIs, SQL, Python, or other data and integration approaches that support scalable automation.
- Experience operating under SDLC, end-user developed tool, model risk, data governance, responsible AI, or similar control frameworks.
Additional Requirements
This role works closely with Technology Financial Operations Office leadership and its TCO, FinOps, data, BI, productivity, portfolio, demand, procurement, headcount, and reporting teams; the SMEs who run the processes being automated; technology, data, and platform partners; and the architecture, security, risk, audit, controls, and responsible AI functions. This role operates in a matrixed environment where requirements shift, processes are still manual, and data quality and controls need attention.
Work Requirements
- Expected to follow applicable State Street office attendance requirements for the role and location.
- Standard business hours, with flexibility as needed to support stakeholders across time zones; limited travel may be required.
Salary Range:
$70,000 - $118,750 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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Salary Context
This $70K-$118K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($94K) sits 55% below the category median. Disclosed range: $70K to $118K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
State Street AI Hiring
State Street has 18 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Cracow, Małopolskie, Poland, Quincy, MA, US, Boston, MA, US. Compensation range: $118K - $217K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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