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About This Role
About the Role
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We're seeking a strategic, hands\-on VP to lead the design and delivery of our enterprise Generative AI platform. Primary depth required in AWS Bedrock, Azure AI Foundry, Azure OpenAI, and Databricks, with strong skills in IaC authorship, Harness CI/CD, FinOps, and AI\-assisted developer tooling. You'll set technical direction, author production\-grade constructs, mentor senior engineers, and enable data scientists and application teams to ship Gen AI solutions at scale.
Key Responsibilities
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AWS Bedrock Platform (Primary)
- Own enterprise Bedrock strategy: model access governance, provisioned throughput, cross\-region inference, multi\-account architecture
- Operationalize Knowledge Bases, Agents, Guardrails, Prompt Management, Flows, and Model Evaluation
- Lead FM lifecycle across Claude, Nova/Titan, Llama, Mistral, and Cohere
- Design RAG on Bedrock with OpenSearch Serverless, Aurora pgvector, and Kendra
- Optimize consumption: on\-demand vs. PT, model routing, prompt caching, token efficiency
Azure AI Foundry \& Azure OpenAI (Primary)
- Own Foundry hub/project architecture, model catalog governance, and quota management
- Lead Azure OpenAI patterns (PTU vs. PAYG, capacity planning, content filters) across GPT\-4o, GPT\-4\.1, and o\-series models
- Architect RAG/agent workloads with AI Search, prompt flow, and Agent Service
- Implement Content Safety, private networking, Entra ID, CMK, and data residency controls
Databricks \& Data\-Centric AI (Primary)
- Own Databricks strategy on AWS and Azure: workspace architecture, Unity Catalog, cluster policies
- Lead Mosaic AI adoption: Model Serving, Vector Search, Feature Store, AI Gateway, MLflow
- Architect fine\-tuning/pretraining pipelines and lakehouse\-native RAG on Delta Lake
- Establish DBU cost controls and serverless governance
Infrastructure as Code \& Golden Paths
- Author L2/L3 CDK constructs (TypeScript/Python), Bicep modules, and Terraform for multi\-cloud AI infra
- Codify secure\-by\-default "golden paths" enabling teams to launch Gen AI workloads in hours
- Standardize config\-as\-code, secrets management, and drift detection
Harness CI/CD
- Own Harness pipelines, templates, and delegates for Bedrock, Azure OpenAI, Foundry, and Databricks Asset Bundles
- Integrate with GitOps, Terraform/CDK/Bicep, OPA policy\-as\-code, and approval gates
- Establish reference pipelines with linting, security scanning, model eval, and cost checks
Developer Experience
- Champion Claude Desktop, MCP servers, and AI coding assistants (Copilot, Cursor, Claude Code)
- Build internal MCP servers exposing enterprise systems to agentic clients
- Define secure usage patterns for regulated environments; measure productivity impact
Governance \& Leadership
- Establish security, compliance, and responsible AI controls (red\-teaming, audit logging, guardrails)
- Build observability across CloudWatch, Azure Monitor, Databricks system tables, Grafana, Datadog
- Partner with Data, MLOps, Security, and Application leadership; recruit and mentor a top\-tier team
Required Skills
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- Bedrock: Knowledge Bases, Agents, Guardrails, Flows, PT; Claude, Nova, Llama, Mistral; RAG and vector stores (OpenSearch, pgvector, Pinecone, Kendra); agent frameworks (Bedrock Agents, LangGraph, Strands)
- Azure AI: Foundry hubs/projects, prompt flow, Agent Service; Azure OpenAI (GPT\-4o/4\.1, o\-series, PTU); AI Search; Content Safety
- Databricks: Mosaic AI, Vector Search, AI Gateway, MLflow, Unity Catalog, Asset Bundles, Delta Lake
- IaC: CDK L2/L3 (TS/Python), Bicep, Terraform (multi\-cloud), secrets management
- CI/CD: Harness (YAML pipelines, delegates, templates, GitOps), OPA/Rego, GitHub Actions, Azure DevOps
- FinOps: Token/PTU/DBU governance, tagging, chargeback, capacity planning, token/model optimization
- Dev Tooling: Claude Desktop, MCP server authoring, Claude Code, Copilot, Cursor
- Cloud: Deep AWS \+ Azure (networking, IAM/Entra ID, private endpoints); Docker/Kubernetes (EKS/AKS); serverless; API design; complementary GCP/Vertex AI
- Data: Delta Lake, Unity Catalog, RAG/fine\-tuning data pipelines, governance/lineage
- Observability: CloudWatch, Azure Monitor, App Insights, Grafana, Arize, Datadog
- Languages: Python (primary), TypeScript
- Leadership: Scaling senior teams hands\-on, executive communication, owning budgets/roadmaps
Education \& Experience
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- Bachelor's or Master's in CS, Engineering, or IT
- 10\+ years in platform/cloud/SRE engineering
- 5\+ years hands\-on AWS and Azure with production depth in Bedrock, Azure OpenAI/Foundry, and Databricks
- 3\+ years in technical leadership or people management
- Financial services or regulated\-industry experience strongly preferred
Preferred Qualifications
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- Published CDK construct libraries, Bicep registries, or Terraform modules consumed enterprise\-wide
- Enterprise\-scale Harness pipeline authoring and rollout
- Fine\-tuning/continued pretraining experience (LoRA, QLoRA, RLHF, DPO) on Mosaic AI, Azure OpenAI, or Bedrock Custom Models
- Model optimization: quantization, distillation, prompt caching, speculative decoding
- PTU/PT capacity planning at scale
- Production MCP servers and Claude Desktop deployments in regulated environments
- Agentic frameworks (Bedrock Agents, Azure AI Agent Service, Databricks Agent Framework, LangGraph, Strands)
- AI safety/guardrail frameworks (Bedrock Guardrails, Azure Content Safety, NeMo, Guardrails AI)
- Measurable FinOps wins (token/DBU/inference cost reduction)
- Open\-source contributions (CDK, Terraform providers, MCP)
- Multi\-tenant AI platforms with chargeback/showback
- Board/regulator\-level presentation experience
Certifications (one or more preferred)
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- AWS: Solutions Architect Pro, ML Specialty, DevOps Engineer Pro
- Azure: AZ\-305, AI\-102, AZ\-400, OP\-100
- Databricks: Data Engineer Pro, ML Pro, or Generative AI Engineer Associate
- GCP: Professional Cloud Architect or ML Engineer (complementary)
- Harness: Certified Expert (CD or Platform)
- Anthropic: Claude Builder or partner credentials
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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Salary Context
This $120K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $120K to $202K.
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.
State Street AI Hiring
State Street has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Quincy, MA, US, Boston, MA, US, Burlington, MA, US. Compensation range: $118K - $217K.
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/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 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).
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 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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