Technical Lead Manager, Data Infra, Gemini AutoCloud, Agentic AI

• Lead and manage a team of 3–6 engineers, guiding project execution, driving on-call and operational excellence, and mentoring junior engineers to foster technical growth and a high-performance team culture.
• Architect, design, and implement end-to-end components for the Quality Flywheel including Agentic AI, Context and Memory, and Hill Climbing engines while upholding project-wide software and system design standards.
• Build and scale robust machine learning infrastructure and platforms to power critical workflows such as large-scale data collection, Gemini post-training, unified benchmarking, and next-generation evaluation systems.
• Define and own the overarching testing architecture and reliability standards to guarantee high availability, fault tolerance, and system quality across production and test environments.
• Partner with cross-functional stakeholders to translate complex, multi-team requirements into simplified, scalable technical architectures and actionable roadmaps.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience with distributed storage.
• 2 years of experience in a people management or team leadership role.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with technical innovation within cross-functional engineering environments.
  
• Experience in 0-1 projects.
  
• Expertise building Gen AI, ML products, Agent frameworks, Agent protocols (ex: MCP, A2A), RAG, Prompt Engineering, Evals.
  
• Strong track record of mentorship and excellent communication skills.
  

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience with distributed storage.
• 2 years of experience in a people management or team leadership role.