Job Purpose

• Lead the development, deployment, and optimization of ML solutions across the organization
• Own and scale ML engineering teams and platforms
• Ensure alignment with business goals and technical excellence
• Drive innovation, efficiency, and enterprise ML adoption 

Key Result Areas / Responsibilities

• Lead design, development, and deployment of scalable ML systems and platforms 
• Drive ML engineering best practices, including lifecycle management
• Collaborate with Data Science, Product, and Engineering teams
• Establish MLOps, CI/CD pipelines, and model monitoring standards
• Mentor and grow ML engineering teams 

Operating Environment

• Works in a cloud-native environment with modern ML frameworks
• Collaborates cross-functionally with:
  • Data Scientists
  • Software Engineers
  • Product Managers
  • Business stakeholders 

Decision Authority

• Own ML engineering roadmap and technology stack
• Responsible for:
  • Hiring and team structure
  • Architecture decisions
  • Delivery quality and timelines
• Decide on vendor and tooling strategy

Skills & Experience

• 10+ years engineering experience, including ML leadership
• Proven experience deploying ML models at scale in production
• Expertise in:
  • ML frameworks 
  • On-prem ML platform and Cloud platforms (Azure/AWS/GCP)
  • MLOps, CI/CD, containerization (Docker, Kubernetes)
• Strong leadership, stakeholder management, and communication skills
• Understanding of ethical AI, privacy, and compliance