JOB TITLE: Senior Development Manager, Platforms Development AI/ML
The Senior Development Manager, Platforms Development AI/ML leads a team of software and ML engineers to build AMP, a next-generation machine learning platform. This role involves providing technical leadership, driving strategy, and managing the execution of scalable AI/ML systems to power Agentic AI features across products. Daily responsibilities include mentoring engineers, collaborating with cross-functional teams to define roadmaps, and overseeing architecture, code quality, and security standards. The position requires expertise in AWS, Azure, or Google Cloud Platform, along with containerization tools like Docker and Kubernetes. Candidates must possess skills in AWS SageMaker, Bedrock, or Azure Machine Learning, MLOps principles, and CI/CD automation. The work focuses on complex agentic AI architectures, including API ecosystems, guardrails, context management, and deep learning models.
Lead and mentor a team of software and ML engineers in developing and deploying AI/ML solutions.
Define and execute the AI/ML platform roadmap in collaboration with cross-functional teams.
Provide technical guidance on the design and architecture of scalable, reliable, and efficient AI/ML systems.
Drive the execution of the platform strategy using agile methodologies and software development best practices.
Create and manage project plans to ensure adherence to budgets, schedules, and performance goals.
Participate in coding reviews and technical problem-solving alongside the engineering team.
Manage the hiring process to attract and onboard top-tier development talent for the AI/ML platform.
What we're looking for
BS/MS in Computer Science, Engineering, or a related field (MS preferred).
12+ years of experience in software development.
3+ years of experience as a manager.
Proven experience leading and mentoring software and ML development teams.
Experience building and running scalable, high-performance systems on cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes).
Experience building and deploying AI models using AWS SageMaker, Bedrock, or Azure Machine Learning.
Experience working with complex agentic AI architectures, API ecosystems, guardrails, and context management.
Understanding of MLOps principles and expertise in CI/CD, automation tools, and machine learning lifecycle management.