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About This Role
About MassDOT:
The 4,000+ employees of Massachusetts Department of Transportation (MassDOT) take great pride in connecting the Commonwealth’s residents and communities. MassDOT is responsible for developing, implementing, and coordinating transportation policies and projects for the Commonwealth of Massachusetts and to efficiently plan, design, construct, and maintain a safe statewide transportation system which effectively meets the transportation needs of the Commonwealth. Information about MassDOT’s culture and career opportunities can be found at mass.gov/massdot-careers.
MassDOT’s divisions include Highway, Registry of Motor Vehicles, Aeronautics, and Rail & Transit. Headquarters (Planning & Enterprise Services) provides business and administrative support and policy leadership for each of the four (4) divisions.
Position Summary:
The Director of Enterprise Data & AI Platforms is a senior leader within the MassDOT Information Technology division, reporting to the Secretariat Chief Information & Technology Officer. This role serves as the technology leader for enterprise data, AI, analytics platforms, and related SAAS platforms.
This leader oversees the end-to-end lifecycle of enterprise AI systems, cloud-based data platforms, and other related Enterprise SAAS Platforms, driving scalable architecture, modern data capabilities, MLOps/LLMOps excellence, responsible AI adoption, and enterprise platform governance across the organization.
The primary work location for this position is 10 Park Plaza, Boston, Massachusetts. The standard work schedule is Monday through Friday, 9:00 AM to 5:00 PM ET. This position follows a hybrid work model, with an expectation of working on-site a minimum of three days per week and remotely on the remaining days, subject to operational business needs.
Core Responsibilities:
Strategy & Value Realization
- Owns the technology strategy, architecture, and delivery of enterprise AI and data platforms, while partnering with business and Chief Data Office (CDO) on use-case prioritization and value realization.
- Identify, prioritize, and scale AI use cases tied to measurable outcomes (operational efficiency, service delivery, customer experience).
- Establish KPIs and maturity models to track the impact and value of AI and data investments.
AI Platform & Engineering Leadership
- Lead the architecture, design, and delivery of scalable AI systems, including GenAI, RAG, and agentic AI solutions.
- Establish and operationalize MLOps/LLMOps frameworks for model lifecycle management, performance, and observability.
- Drive a cloud-first platform strategy, enabling reusable, secure, and scalable AI capabilities across the enterprise.
- Monitor emerging technologies (e.g., cloud services, GPUs, AI tooling) and guide platform evolution.
Data Engineering & Platform Oversight
- Lead development of modern cloud data platforms (data lake, warehouse, real-time pipelines on AWS, Snowflake).
- Ensure availability of AI-ready data through robust data pipelines, integration frameworks, and multimodal data support.
- Oversee ETL/ELT processes, real-time data processing, and data observability practices.
- Establish best practices for data architecture, integration, and platform scalability.
Governance, Risk & Responsible AI
- Build and enforce enterprise Data & AI governance frameworks, including data quality, lineage, privacy, and access controls.
- Establish responsible AI practices ensuring transparency, fairness, security, and regulatory compliance.
- Ensure alignment with EOTSS standards and Commonwealth policies.
- Partner with legal, audit, and security teams to ensure adherence to regulatory and risk requirements.
Operating Model, Adoption & Change Leadership
- Establish and govern an enterprise AI portfolio and delivery model balancing innovation with standardization and control.
- Lead organizational change management efforts to enable adoption of AI solutions at scale.
- Promote AI literacy and workforce development to drive effective and responsible use of AI technologies.
- Enable cross-functional collaboration to embed AI into business processes and decision-making.
Leadership & Cross-Functional Collaboration
- Lead multiple engineering and platform teams responsible for enterprise data, AI, and analytics.
- Partners across IT, business units, data office, security, HR, and operations to co-design and deliver mission-critical solutions.
- Provide executive-level communication and thought leadership on AI and data strategy.
- Support resilience planning, including disaster recovery and continuity of operations for AI and data platforms.
Preferred Qualifications & Experience
- At least 10 years of progressive experience in IT, including data engineering, cloud platforms, and AI/ML delivery, with demonstrated success leading large-scale transformation initiatives.
- Proven senior leadership experience managing large, cross-functional data and AI engineering teams.
- Proven ability to manage enterprise technology portfolios, prioritize AI and data investments, establish performance metrics, and demonstrate measurable business value.
- Demonstrated experience developing and executing enterprise technology strategies, architecture roadmaps, and scalable platform solutions aligned with organizational objectives.
- Strong knowledge of MLOps and LLMOps frameworks, including model deployment, monitoring, performance optimization, and lifecycle management in production environments.
- Demonstrated success translating AI and data capabilities into measurable business outcomes particularly within regulated, complex, or mission-critical environments.
- Exceptional executive communication, stakeholder management, and organizational change leadership skills, with the ability to influence decision-making and drive alignment across diverse business and technical stakeholders.
- Deep expertise in modern enterprise data architectures, cloud platforms, AI/ML technologies, and Software-as-a-Service (SaaS) solutions, including platforms such as AWS, Snowflake, and ServiceNow.
- Strong understanding of enterprise data governance, data privacy and security regulations, and responsible AI principles and frameworks with demonstrated ability to support compliance, risk management, and ethical technology adoption.
Education and Certification(s):
- Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Engineering, Artificial Intelligence, or a related field preferred, but not required.
- Professional certifications in cloud computing, data engineering, and artificial intelligence, including AWS certifications, Snowflake certifications, and AI/ML-related certifications, are preferred but not required.
A finalist for this position must be able to pass a comprehensive background check including a criminal record check, Department of Revenue (DOR) check, and employment references.
Qualifications
Minimum Entrance Requirements
This requisition will remain open until filled; however, first consideration will be given to those applicants that apply within the first 14 days.
Current MassDOT employees should use their internal MassCareers account to apply.
All job applications must be submitted online through MassCareers to be considered.
Please provide a complete, accurate and current resume / application for MassDOT to review in order to determine if your submitted materials meet the minimum entrance requirements for the position.
Applicants must have at least (A) six (6) years of full-time or, equivalent part-time, professional, administrative, supervisory, or managerial experience in business administration, business management, public administration, public management, clinical administration or clinical management of which (B) at least two (2) years must have been in a supervisory or managerial capacity or (C) any equivalent combination of the required experience and substitutions below.
Substitutions
I. A certificate in a relevant or related field may be substituted for one (1) year of the required (A) experience.
II. A Bachelor's degree in a related field may be substituted for two (2) years of the required (A) experience.
III. A Graduate degree in a related field may be substituted for three (3) years of the required (A) experience.
IV. A Doctorate degree in a related field may be substituted for four (4) years of the required (A) experience.
- For questions regarding the job posting, please email the MassDOT Talent Acquisition Team at [email protected].
- For general questions regarding MassDOT, call the Human Resources Service Center at 857‐368‐4722.
- For a disability‐related reasonable accommodation or alternative application method, call the ADA Coordinator, Lucy Bayard, at 857‐274‐1935.
Comprehensive Benefits
When you embark on a career with the Commonwealth, you are offered an outstanding suite of employee benefits that add to the overall value of your compensation package. We take pride in providing a work experience that supports you, your loved ones, and your future.
Want the specifics? Explore our Employee Benefits and Rewards!
An Equal Opportunity / Affirmative Action Employer. Females, minorities, veterans, and persons with disabilities are strongly encouraged to apply.
The Commonwealth is an Equal Opportunity Employer and does not discriminate on the basis of race, religion, color, sex, gender identity or expression, sexual orientation, age, disability, national origin, veteran status, or any other basis covered by appropriate law. Research suggests that qualified women, Black, Indigenous, and Persons of Color (BIPOC) may self-select out of opportunities if they don't meet 100% of the job requirements. We encourage individuals who believe they have the skills necessary to thrive to apply for this role.
PESJobs
Official Title: Administrator VIII Primary Location
: United States-Massachusetts-Boston-10 Park Plaza Job
: Information Systems and Technology Agency
: Massachusetts Department of Transportation Schedule
: Full-time Shift
: Day Job Posting
: Oct 15, 2026, 12:19:39 PM Number of Openings
: 1 Salary
: 111,961.20 - 173,020.79 Yearly If you have Diversity, Affirmative Action or Equal Employment Opportunity questions or need a Reasonable Accommodation, please contact Diversity Officer / ADA Coordinator: Derrick Mann - 8573688541 Bargaining Unit: M99-Managers (EXE) Confidential: No Potentially Eligible for a Hybrid Work Schedule: No
Salary Context
This $111K-$173K range is below the median 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 Commonwealth of Massachusetts, 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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $111K to $173K.
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.
Commonwealth of Massachusetts AI Hiring
Commonwealth of Massachusetts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $173K - $173K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 tracked positions.
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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