Advanced Data & AI Architect (P)
Job ID: 14736
Business Unit: MTA Headquarters
Location: New York, NY, United States
Regular/Temporary: Regular
Department: IT Strategy/Architect
Date Posted: Feb 18, 2026
Description
JOB TITLE:
Advanced Data & AI Architect (P)
SALARY RANGE:
$152,215 - $179,664
DEPT/DIV:
MTA IT Strategy & Architecture
SUPERVISOR:
Senior Director Enterprise Information & Data Architecture (P)
LOCATION :
Various / 2 Broadway , New York, NY 10004
HOURS OF WORK:
9:00 am - 5:30 pm (7.5 hours/day) or as required
This position is eligible for telewor k which is currently 2 days per week . New hires are eligible to apply 30 days after their effective date of hire.
Opening
The Metropolitan Transportation Authority is North America's largest transportation network, serving a population of 15.3 million people across a 5,000-square-mile travel area surrounding New York City, Long Island, southeastern New York State, and Connecticut. The MTA network comprises the nation's largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. MTA strives to provide a safe and reliable commute, excellent customer service, and rewarding opportunities.
About Us
The MTA transportation network has a very large system and infrastructure for financial, business, automated train, transportation, power, and physical security. The MTA IT Strategy & Architecture is centrally responsible for providing a full range of Information and Operational Technology services to the MTA agencies and administrative units through its operating and support units. Services are provided on a 7/24/365 basis in support of the MTA organization and its ridership.
The Enterprise Strategy & Architecture (ESA) team, part of the MTA IT Department, is responsible for guiding and aligning technology strategies across the organization. The team drives innovation and operational efficiency, ensuring IT initiatives support individual agency business requirements while advancing the forward-looking objectives of the MTA enterprise. ESA comprises multiple domains, including Enterprise Architecture, Cloud & Platform, Data & AI, Application, and Network Architecture, all working together to deliver integrated and effective technology solutions.
Summary
The Advanced Data and AI Architect is responsible for designing and implementing advanced, scalable data and AI solutions that support the MTA's business operations and strategic decision-making. This role requires deep expertise in data architecture, machine learning, and cloud technologies, with a strong focus on technical architecture, solution design, and hands-on development across data engineering, data science, and MLOps domains. This role calls for extensive experience with modern data platforms, cloud ecosystems, and AI/ML frameworks, highly skilled at translating business needs into robust technical solutions, and will actively contribute to both the design and implementation phases in close collaboration with engineering teams.
Responsibilities
Design and implement secure, scalable, and cost-efficient data, analytics, and AI/ML architectures to support Data and AI initiatives.
Translate solution requirements into detailed technical designs and implement high-quality, production-ready systems.
Participate in architectural review processes to ensure solutions align with MTA's enterprise data strategy, technical standards, and best practices.
Build and maintain data pipelines, analytical data models, and ML workflows using modern cloud-native technologies.
Actively contribute to development efforts, coding in Python, PySpark , and related technologies; review, refactor, and optimize code for efficiency in performance, scalability, and maintainability.
Develop MLOps workflows including CI/CD pipelines, model deployment, model monitoring, and retraining strategies.
Build and implement data integration frameworks, APIs, and orchestration workflows to support data-driven AI/ML initiatives.
Ensure all solution designs comply with data governance policies, data security protocols, and relevant regulatory and compliance requirements.
Continuously optimize storage, computing, and operational costs across data engineering and AI workloads.
Work closely with data engineers, data scientists, cloud architects, and business stakeholders to deliver end-to-end AI and data solutions that drive business value.
Collaborate with cross-functional teams to identify and prioritize AI opportunities that deliver the highest business impact.
Educate teams and stakeholders on the practical applications of AI and machine learning within the organization.
Implement AI models and data solutions with a strong focus on data privacy, security, and compliance with regulations like GDPR and CCPA.
Enforce security protocols to protect sensitive data and ensure responsible AI usage.
Stay up to date with the latest advancements in AI/ML technologies, frameworks, and methodologies, driving innovation in data-driven solutions.
Identify opportunities to leverage emerging AI trends, such as deep learning, NLP, or computer vision, to address business needs.
May need to work outside of normal work hours supporting 24/7 operations (i.e., evenings and weekends).
Performs other duties and tasks as assigned.
Review invoices and approve them if the work meets contractual standards.
Address performance issues with the contractor when possible.
Escalate issues to other parties as needed.
Abide by MTA attendance expectations and requirements by attending regularly and reliably.
Provide technical advice to project teams and mentor less experienced staff to foster talent development.
Observing the work performed by the contractor.
Required Qualifications
Education: Bachelor's degree and a minimum of 8 years of relevant experience. An equivalent combination of education and experience may be considered in lieu of a degree.
Certification(s): Requires at least one certification in the current platform/domain/technical skill. Possible certifications could be, but are not limited to:
Relevant Certifications
TOGAF (The Open Group Architecture Framework)
SAS Certified Data Scientist
Certified Information Management Professional (CIMP)
AWS Certified Machine Learning - Specialty
Certified Data Management Professional (CDMP)
Google Professional Machine Learning Engineer
IBM Certified Data Architect - Big Data
Microsoft Certified: Azure AI Engineer Associate
Oracle Certified Professional, Oracle Database Architect
AWS Certified Solutions Architect
Cloudera Certified Data Professional (CCDP)
Google Professional Cloud Architect
Databricks Certified Data Engineer Associate
Microsoft Certified: Azure Solutions Architect Expert
Oracle Data Management and Modeling
Microsoft Certified: Azure Data Engineer Associate
CIMP Data Modeling
Amazon Web Services (AWS) Certified Big Data - Specialty
Dataversity's Data Modeling Certified Professional (DMCP)
IBM Certified Data Engineer
Technical Skills
Experience working with Enterprise Architecture
Experience in the ML/AI domain
Adept in Python, Data Science, Data Engineering & MLOPS?
Adept in cloud architecture and development
Adept in cloud operation & management
Adept in virtualization and cloud platforms.
Adept in cloud services (e.g., analytics).
Adept in cloud computing, cloud solutions, cloud automation, cloud services (e.g., analytics).
Adept in analyzing storage needs, performance tuning, and capacity planning
Adept in Disaster Recovery principles and tools, including complex recovery environments and comprehensive risk assessments.
Adept in computing services management
Adept in data services management
Adept in software development
Adept in computer science.
Adept in Databricks, PySpark , Python, R, and AWS components such as EventBridge , Lambda, etc.
Adept in RESTful API design and implementation?
Adept in Web framework like Fast API/Tornado/Flask, etc.
Adept in designing MLOps platforms and architecting big data systems on GCP cloud.?
Adept in designing post-deployment model management framework, e.g., model monitoring tools, workflows for feature drift, error analysis of models?
Adept in designing CI/CD pipelines (Jenkins) for the deployment of Data Engineering and ML jobs workflow.?
Adept in orchestration frameworks like Airflow, Cloud Composer, DataProc Serverless for PySpark jobs, etc.?
Adept in DMBoK
Data Science knowledge and familiarity with ML libraries such as Pandas, Scikit, TensorFlow, xgboost , time series frameworks like prophet/or equivalent frameworks?
Knowledge of design patterns and architecture, data science, and machine learning best practices
Working knowledge of ML frameworks, such as Vertex, Kubeflow, MLflow , CloudRun, etc.
Hands-on design and coding is required, review code, refactor if necessary, and play a hands-on role in coding critical areas yourself
Experience with relational databases like Big Query, cloud environments, and a good understanding of optimizing storage cost/query cost while designing data engineering workflows
Good knowledge of Kubernetes, container technologies, Docker registries, and applying them in the @context of machine learning systems.?
In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - Cloud Composer, Dataproc , serverless, BigQuery, Cloud Run, Vertex, vertex pipelines, GKE.
Premium Technical Skills
Languages & Frameworks: Python, PySpark , SQL, RESTful API development ( FastAPI , Flask, Tornado).
ML & AI: Pandas, Scikit-learn, TensorFlow, XGBoost , Prophet (or equivalent), MLflow , Kubeflow, Vertex AI, CloudRun .
Data Engineering : Airflow, Cloud Composer, DataProc Serverless, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Power BI, Azure Machine Learning, Cosmos DB, Azure DevOps, Data Lake Storage, Azure Purview
Cloud Platforms: Strong experience in GCP (Vertex, GKE, DataProc , Cloud Composer), familiarity with AWS ( EventBridge , Lambda), Microsoft Azure (AI Foundry).
Orchestration & Automation: Airflow, Jenkins CI/CD, containerization (Docker, Kubernetes).
MLOps : Model deployment, monitoring, feature drift detection, error analysis workflows.
Databases: Big Query, relational and NoSQL data stores, SQL Server, Oracle, DB2
Best Practices: Design patterns, cloud cost optimization, secure coding, and architecture principles.
Behavioral Skills
Advanced in establishing and maintaining effective working relationships with employees at all levels within the organization, and with both internal and external customers.
Advanced in interpersonal, verbal, and written communication skills, with the ability to effectively collaborate with both technical and non-technical peers.
Advanced in communicating effectively, both orally and in writing, to interact with team members, customers, management, and support personnel (technical and non-technical)
Adept in identifying and analyzing risks and developing effective mitigation strategies.
Adept in critical thinking, problem-solving, and decision-making skills.
Adept in active listening, attention to detail, customer service, prioritization, and problem-solving skills.
Adept in hands-on experience with related tools.
Adept in working independently and strategically.
Adept technical knowledge and diverse skillset to understand various technologies, systems, and potential risks.
Adept in managing multiple projects simultaneously and prioritizing tasks based on urgency and impact.
Adept at working under pressure and meeting deadlines individually and collaboratively. Thinks logically, assesses problems, and is results-oriented.
Adept in identifying complex business and technology risks and associated vulnerabilities.
Competencies
Core Competency
Proficiency Level
Competency Definition
Cultivates Innovation
Adept
Creating new and better ways for the organization to be successful
Customer Focus
Adept
Building strong customer relationships and delivering customer-centric solutions
Communicates Effectively
Expert
Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
Tech Savvy
Advanced
Anticipating and adopting innovations in business-building digital
and technology applications
Technical Skills
Advanced
Specialized knowledge and expertise on tools, programs, domains, platforms, and products used for specific tasks
Values Diversity
Advanced
Recognizing the value that different perspectives and cultures bring to an organization
Other Information
Pursuant to the New York State Public Officers Law & the MTA Code of Ethics, all employees who hold a policymaking position must file an Annual Statement of Financial Disclosure (FDS) with the NYS Commission on Ethics and Lobbying in Government (the "Commission").
Equal Employment Opportunity
MTA and its subsidiary and affiliated agencies are Equal Opportunity Employers, including with respect to veteran status and individuals with disabilities.
The MTA encourages qualified applicants from diverse backgrounds, experiences, and abilities, including military service members, to apply.
