Applied AI SRE III - PxE GPS

About the role

The Applied AI Site Reliability Engineer III plays a crucial role in ensuring the reliability, performance, and operational integrity of high-visibility products and platforms. This position requires a hands-on approach to managing cloud platform engineering, observability, and performance and reliability engineering, with a focus on operating AI and agentic workloads.

Responsibilities

• Drive accountability for reliability, performance, and cost outcomes, measured in service-level objectives and error budgets.
• Operate products, platforms, and environments to meet their Service Level Objectives (SLOs) within budget, track incident trends, and prioritize work that improves reliability.
• Lead the design of observability, performance, and resilience testing, and operational tooling, and own the admission of systems into production.
• Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against.
• Adopt a mindset that favors action and evidence over extensive planning, adopting a leaning-forward approach to reliability through incremental, measurable improvements.
• Work collaboratively with cross-functional partners, including engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture.
• Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business.

Requirements

• A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline.
• 5+ years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production.
• Experience in defining and owning Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs); error budgets; incident command and on-call; building and operating production observability; environment integrity and drift prevention; and segregation-of-duties controls.
• Experience with cloud-native engineering and cloud platform ownership on any of the cloud hyperscalers such as Azure, AWS, or GCP, including their AI/ML services and container orchestration.
• Prior experience operating AI/ML and agentic workloads in production, understanding their reliability failure modes and MLOps/LLMOps.
• Experience with load and performance testing, chaos engineering, capacity planning, autoscaling, and cloud/AI cost engineering.
• Understanding of Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, object-oriented programming, data structures, algorithms, and code instrumentation, as well as AI-augmented spec-driven development.
• Experience with methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and multi-agent orchestration tools.

Qualifications

• Legal authorization to work in the United States without the need for employer sponsorship.
• U.S. Citizenship or Green Card holder.

Skills

• Excellent interpersonal and organizational skills.
• Advanced proficiency in Python, Go, Bash, Java, C#, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, CI/CD, observability stacks, cloud-native engineering, and AI/ML services.
• Experience with cloud platform ownership, observability, performance and capacity engineering, chaos engineering, and cloud/AI cost engineering.
• Expertise in applied AI and agentic workloads, including AI/ML services, MLOps/LLMOps, and AI control plane.
• Ability to communicate complex technical concepts clearly and inspire others through well-structured arguments and trade-offs supported by evidence.

Benefits

Details about benefits are not provided in the job posting.

Pay

Details about pay are not provided in the job posting.

Schedule

Details about schedule are not provided in the job posting.