• Lead and participate in the architectural design of complex features early in the development lifecycle.
• Translate customer requirements and roadmap priorities into technical solutions, tasks, timelines, and resource plans.
• Develop, integrate, and maintain full stack applications supporting ML/Ops pipelines and data-driven systems.
• Design and implement scalable APIs and services to support machine learning model deployment and inference.
• Develop and maintain data pipelines, ETL processes, and data storage solutions for large-scale datasets.
• Collaborate with data scientists and ML engineers to operationalize models within production environments.
• Optimize application and system performance for scalability, reliability, and efficiency, including edge and distributed environments when applicable.
• Conduct peer reviews and establish coding standards to improve overall code quality and maintainability.
• Guide development testing, exploratory testing, automated testing, and validation strategies.
• Own code in production environments, respond to incidents, and lead root cause analysis and continuous improvement efforts.
• Ensure security, compliance, and governance are maintained throughout the development lifecycle.
• Perform technical planning, system integration, verification and validation, and risk assessments across system components.
• Mentor and develop junior and mid-level engineers, fostering technical growth and high-performing teams.
• Drive adoption of modern ML/Ops practices, tools, and automation frameworks across the team.