Description

• Collaborate with cross-functional teams to understand business needs, define requirements, and translate them into effective AI solutions.
• Develop and maintain machine learning models, ensuring they are accurate, efficient, and scalable.
• Design and implement AI solutions using large datasets, focusing on data security and user privacy.
• Orchestrate the development and maintenance of CI/CD pipelines for deploying and testing AI applications.
• Ensure governance, security, and compliance of all cloud-based AI solutions.
• Implement best practices for AI model deployment, monitoring, and maintenance.
• Write well-documented, clean, and maintainable code, adhering to best practices and methodologies.
• Continuously iterate and improve upon existing models and solution design/implementation processes, incorporating feedback and lessons learned to enhance accuracy, performance, scalability, and security.
• Stay up to date with the latest advancements in AI, machine learning, and Azure services, and apply this knowledge to improve our solutions.
• Defines and manages scoping and requirements definition and ensures traceability to source.
• Designs, codes, verifies, tests, documents, amends, and refactors complex programs/scripts and integration software services. Uses appropriate modeling techniques following agreed software design standards, guidelines, patterns, and methodology. Testing: Develops and executes test plans and test cases; implements scalable and reliable automated tests and frameworks.
• Configuration Management: Develops, configures, and maintains tools to identify, track, log, and maintain accurate, complete, and current system information; develops and maintains associated operational documentation.
• Application Support: Maintains application support processes and uses application management software tools to investigate issues, prioritize and diagnose incidents, collect performance statistics, and create reports.
• Project/Product Management: Defines, documents, and executes small projects or sub-projects. May act as product owner for one or more lower-value products or services, managing elements of the product life cycle to meet customer/user needs and achieve financial or other targets.
• Quality & Safety Assurance: Plans, organizes, and conducts quality and safety assessments, and suggests opportunities for improvement. Contributes to identifying, analyzing, and documenting hazards and safety risks.
• Supplier Management: Monitors and reports on supplier performance, customer satisfaction, adherence to security requirements, and market intelligence.
• Skills Development: Continuously develops and maintains personal knowledge of software engineering practices, emerging trends, and technologies.
• May mentor less experienced staff
• Performs other duties and tasks.
• May need to work outside of normal work hours (i.e., evenings and weekends)
• Travel may be required to other MTA locations or external sites.

• Hands-on experience AI, ML, model development and deployment in a business setting.
• Strong knowledge of machine learning architectures.
• Strong programming skills in languages such as Python, and familiarity with relevant libraries (e.g., TensorFlow, NumPy, PyTorch).
• Experience with deep learning and neural network development.
• Strong statistical foundation, with in-depth knowledge of supervised, unsupervised, and semi-supervised machine learning models.
• Good understanding and experience with AI services such as AI Search, Document Intelligence, NLP, and conversational AI.
• Experience working with Generative AI models, with a deep understanding of their capabilities, prompting strategies, model performance, and inherent limitations.
• Experience writing complex, highly optimized SQL queries.
• Solid understanding of software engineering fundamentals. Experience with software development best practices, including version control, automated testing, and continuous integration/continuous deployment (CI/CD).
• Experience building, configuring, and maintaining secure ML cloud infrastructure. Proficiency with Infrastructure as code technologies such as Terraform.
• Experience with container services e.g., Kubernetes.
• Good understanding of cloud security best practices.

• Education: Bachelor’s Degree 
• Experience: At least 3 years of relevant experience. An equivalent combination of education and experience may be considered instead of a degree. 

• Certified Software Engineer or equivalent, technical certifications relevant to the specific position (and software products and toolsets used in that position), experience with System Development Life Cycle with the specific methodologies in use in the Department.
• A software engineering industry certification, including but not limited to: Certified Software Development Professional (CSDP), Certified Secure Software Lifecycle Professional (CSSLP), Amazon Certified: AWS Certified Developer, Microsoft Certified: Azure Developer, Certified Software Test Professional (CSTP)
• Experience administering and developing workflows and specialized UIs in Alfresco or other Document Management systems.

• Data Structures and Algorithms (Thorough Knowledge/Fully Proficient)
• Database Management (Thorough Knowledge/Fully Proficient)
• Web Development (Thorough Knowledge/Fully Proficient)
• DevOps (Thorough Knowledge/Fully Proficient)
• Operating Systems (Thorough Knowledge/Fully Proficient)
• Cybersecurity, including encryption and authentication (Thorough Knowledge/Fully Proficient)
• Cloud Computing (Thorough Knowledge/Fully Proficient)

• Active Listening, Attention to Detail, Customer Service, 
• Prioritization, Problem-Solving, Effective Verbal and Written Communication