Director, Enterprise Data & AI Platforms

Commonwealth of Massachusetts•Boston, MA

9h•Hybrid

About The Position

The Director of Enterprise Data & AI Platforms is a senior leader within the MassDOT Information Technology division and reports to the Secretariat Chief Information & Technology Officer. The role provides technology leadership for enterprise data, AI, analytics, and related SaaS platforms, overseeing their end-to-end lifecycle. The director drives scalable architecture, modern data capabilities, MLOps/LLMOps excellence, responsible AI adoption, and enterprise platform governance across MassDOT. The position owns the technology strategy, architecture, and delivery of enterprise AI and data platforms, partnering with business teams and the Chief Data Office on use-case prioritization and value realization. It leads development of scalable AI systems and cloud data platforms, establishes governance and responsible AI practices, and supports adoption through organizational change, AI literacy, and cross-functional collaboration. The director also leads multiple engineering and platform teams and communicates strategy to executive stakeholders. The primary work location is 10 Park Plaza, Boston, Massachusetts. The position follows a hybrid work model, with at least three days per week on-site and the remaining days remote, subject to operational business needs. The standard schedule is Monday through Friday, 9:00 AM to 5:00 PM ET. A finalist must pass a comprehensive background check, including criminal record, Department of Revenue, and employment reference checks.

Requirements

At least 10 years of progressive IT experience, 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.
Ability to manage enterprise technology portfolios, prioritize AI and data investments, establish performance metrics, and demonstrate measurable business value.
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.
Success translating AI and data capabilities into measurable business outcomes, particularly in regulated, complex, or mission-critical environments.
Exceptional executive communication, stakeholder management, and organizational change leadership skills, with the ability to influence decisions and build alignment across diverse business and technical stakeholders.
Deep expertise in modern enterprise data architectures, cloud platforms, AI/ML technologies, and SaaS solutions, including AWS, Snowflake, and ServiceNow.
Strong understanding of enterprise data governance, data privacy and security regulations, and responsible AI principles and frameworks, with ability to support compliance, risk management, and ethical technology adoption.
Applicants must meet the stated minimum entrance requirements: at least six years of full-time or equivalent part-time professional, administrative, supervisory, or managerial experience in a listed administration or management field, including at least two years in a supervisory or managerial capacity, or an equivalent combination of experience and substitutions.
A finalist must pass a comprehensive background check, including a criminal record check, Department of Revenue check, and employment references.

Nice To Haves

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, or artificial intelligence, including AWS, Snowflake, and AI/ML-related certifications; preferred but not required.

Responsibilities

Own the technology strategy, architecture, and delivery of enterprise AI and data platforms, partnering with business teams and the Chief Data Office on use-case prioritization and value realization.
Identify, prioritize, and scale AI use cases tied to measurable outcomes such as operational efficiency, service delivery, and customer experience.
Establish KPIs and maturity models to track the impact and value of AI and data investments.
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 that enables reusable, secure, and scalable AI capabilities across the enterprise.
Monitor emerging technologies, including cloud services, GPUs, and AI tooling, and guide platform evolution.
Lead development of modern cloud data platforms, including data lakes, warehouses, and real-time pipelines on AWS and Snowflake.
Ensure availability of AI-ready data through 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.
Build and enforce enterprise Data & AI governance frameworks, including data quality, lineage, privacy, and access controls.
Establish responsible AI practices that ensure transparency, fairness, security, and regulatory compliance.
Ensure alignment with EOTSS standards and Commonwealth policies.
Partner with legal, audit, and security teams to meet regulatory and risk requirements.
Establish and govern an enterprise AI portfolio and delivery model that balances 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 support effective and responsible use of AI technologies.
Enable cross-functional collaboration to embed AI into business processes and decision-making.
Lead multiple engineering and platform teams responsible for enterprise data, AI, and analytics.
Partner across IT, business units, the 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.