ML Platform Engineer

Responsibilities

• Designs, develops, tests, and maintains scalable software, machine learning, and agentic AI platform solutions within a defined technical domain.
• Builds and supports multi‑tenant ML and agentic platforms using established engineering and MLOps practices.
• Delivers reliable, secure, and high‑quality solutions while collaborating with cross‑functional teams to execute well‑scoped initiatives and enhance platform capabilities.
• Implements well‑scoped features and enhancements using established coding standards, architectural patterns, and development best practices.
• Contributes to the reliability, scalability, and performance of applications by writing high‑quality, maintainable code and participating in peer code reviews.
• Troubleshoots, debugs, and resolves software defects and production issues within the area of responsibility, applying root‑cause analysis as needed.
• Participates in the full software development lifecycle, including requirements refinement, design discussions, development, testing, deployment, and support.
• Applies secure coding practices, testing strategies, and documentation standards to ensure software quality and compliance with team guidelines.

• Bachelor’s degree and 3 years of experience or equivalent education and software engineering training or experience.
• In‑depth knowledge of information systems with the ability to identify, apply, and implement IT best practices.
• Understanding of key business processes and competitive strategies related to the IT function.
• Bachelor’s degree in computer science, computer engineering, or related field with eight years of experience, or equivalent combination of education and work experience.
• Strong foundation in software engineering, including data structures, algorithms, system design, and enterprise application development, with experience scaling solutions from concept to production.
• Experience designing and developing multi‑tenant platforms with capabilities for tenant isolation, governance, scalability, and support for multiple teams and use cases.
• Hands‑on experience with ML platform components, including feature stores, model training pipelines, model registry, inference services, and monitoring frameworks.
• Proven experience with MLOps and platform engineering practices, including CI/CD for ML, automated deployment, lifecycle management, and reproducibility.
• Experience building scalable, secure, and cost‑efficient cloud‑based platforms (e.g., Azure, AWS), including architecture patterns for multi‑tenant deployments.
• Experience designing and implementing agentic AI platforms or frameworks, including agent orchestration, multi‑agent workflows, tool integration (APIs, retrieval systems), and memory/reasoning patterns using LLMs is a plus.
• Knowledge of governance, security, and responsible AI practices, including data/model governance, regulatory considerations (especially in financial services), and controls for monitoring, auditability, and safe AI adoption is a plus.