Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience with large language models (LLMs), including prompt engineering, agent development, evaluation frameworks, and deployment.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- Experience driving organization-wide initiatives (e.g., migration to new stacks, infra-wide efficiency drives) that deliver measurable improvements to engineering velocity and business outcomes.
- Experience with cross-organizational collaboration and balancing engaging priorities, with a passion for advancing safe, beneficial AI systems through creative technical applications.
- Proficiency in C , Java, or Python, with a track record of shipping production-grade code to users.
- Ability to partner with product area leadership (Directors/Principal Engineers) to translate ambiguous business ambitions into standardized technical roadmaps.
About the jobThe US base salary range for this full-time position is $207,000-$300,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .
Responsibilities - Partner with leadership to decompose high-level business ambitions into scalable technical blueprints.
- Lead the design and implementation of autonomous, agentic workflows to solve the most complex non-linear challenges.
- Be responsible for delivering model context protocol servers, sub-agents, skills, connectors, agentic wrappers, and safety guardrails required to move agents from experimental pilots to production-grade reliability.
- Codify repeatable deployment patterns and drive insights to Product and Engineering.
- Identify high-value agents, skills, or connectors built in isolation and re-engineer them as scalable capabilities. Turn one-off wins into universal standards that harden and expand Google's engineering ecosystem.
