Our Mission

Day to Day

Responsibilities

• Design, implement, and maintain platform services that provide secure, scalable, and reliable access to LLM and SLM providers across multiple cloud environments.
• Contribute to multi-provider access, failover, and rate-limiting capabilities across cloud platforms (Azure, AWS, and GCP).
• Drive improvements to the AI Guardrails Platform, such as content moderation and Personally Identifiable Information (PII) redaction
• Facilitate technical discussions and contribute architectural decisions at the service and system level.
• Identify and implement reliability improvements through SLOs, observability tooling, alerting, and runbook development.
• Collaborate with engineers, product managers, and governance partners across teams to deliver compliant, production-ready AI systems.
• Share knowledge and provide technical guidance with teammates.

Skills/Competencies

• Requires a Bachelor’s degree in Computer Science, Mathematics, Computer Engineering, or Electrical Engineering, and a minimum of 5 years of related experience; or a Master’s degree with a minimum of 3 years of experience; or a PhD without experience
• Experience building or operating large-scale distributed systems, ideally within platform or infrastructure environments
• Proficiency with cloud infrastructure across major providers (AWS, GCP, or Azure), including deployment, monitoring, and operations
• Experience with or interest in AI/ML systems, including LLM-based services, prompt engineering, or AI safety approaches
• Familiarity with reliability engineering practices such as SLOs, error budgets, observability tools, and on-call readiness
• Solid grounding in data structures, algorithms, and core computer science principles, with a track record of solving complex technical problems
• Demonstrated ownership, accountability, and a proactive mindset, paired with curiosity and a drive to continuously learn

Salary Range Transparency

Salary Range Disclaimer

The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. The range(s) listed is just one component of Indeed's total compensation package for employees. Other rewards may include quarterly bonuses, Restricted Stock Units (RSUs), a Paid Time Off policy, and many region-specific benefits. Compensation may also vary based on where a role is performed, as work locations are grouped into geographic pay tiers to reflect cost of labor differences in different geographic markets. Candidates can view geographic pay tiers by location on our career site (https://www.indeed.com/careers/paytiers), and recruiters can confirm how location is considered for a specific role.

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Reference ID: 46965

Reference ID: 46965