About Level AI
Level AI is on a mission to turn every customer interaction into a strategic advantage. Our AI-native platform helps enterprises transform contact centers from cost centers into engines of customer intelligence, operational efficiency, and business growth. By combining advanced AI with deep domain understanding of customer experience, Level AI empowers teams to unlock actionable insights, automate workflows, and deliver more consistent, higher-quality support across the customer journey.
Headquartered in Mountain View, California, Level AI is a Series C company backed by leading investors, including Battery Ventures and ENIAC. Our platform leverages Large Language Models and Custom Small Language Models (SLMs) to power AI Agents across the entire CX journeyโcustomer-facing agents, agent-assist, and backend automationโalong with deep conversation analytics for QA, coaching, and insights.
About the role
The Senior SRE will be positioned at the intersection of backend engineering, infrastructure operations, and FinOps. The role is explicitly broader than a traditional DevOps engineer and explicitly more hands-on than a pure architect.
What you'll be liable for:
Infrastructure cost efficiency and FinOps. Own the continued reduction of Kubernetes overprovisioning, drive right-sizing programs, and maintain the cost telemetry that backend teams use to make decisions.
GPU throughput optimization. Run a structured experimentation program on on-premise GPU clusters, partnering with AI service owners. Led by the Engineering leadership, with this role providing the experimental bandwidth.
Backend enablement, not ownership absorption. Build the tooling, dashboards, and processes that let backend teams from other groups own their own cost and reliability budgets. The deliverable is leverage, not headcount-shaped work.
Reliability instrumentation. As the infra team owns most of the instrumentation across new and offline flows, this role takes a central seat in making sure that surface area is captured properly for both cost-at-scale and reliability.
Selective security workstreams. Take on a defined slice of the active security work so that senior DevOps engineers are not the single point of execution for security-adjacent platform changes.
We'd love to explore more about you if you have:
This role explicitly requires 4-5 years of hands-on systems experience. We are not looking for someone who will lean entirely on AI tooling to discover what to do; we are looking for someone who already knows what to ask and can use AI tooling as a force multiplier on top of that judgement.
Backend engineering depth: production experience in Python, Go/Rust, comfortable owning services end to end, able to read and reason about backend code across teams.
Kubernetes at scale: scheduler behaviour, resource requests/limits, HPA/VPA, node pool design, cost-aware autoscaling (Cast AI, Karpenter, or equivalent).
Cloud and on-premise infrastructure: GCP fluency, IaC (Terraform), CI/CD, and comfort operating in hybrid setups, including on-prem GPU clusters.
GPU workload understanding: familiarity with throughput profiling, batching, KV-cache behavior, inference server tuning, and GPU utilisation metrics.
Observability and reliability: metrics, traces, logs, SLOs, and the discipline to instrument systems properly rather than reactively.
FinOps mindset: demonstrated history of converting infrastructure choices into measurable cost outcomes.
Security baseline: able to take on platform-security workstreams without requiring constant handoff to the DevOps team.
Can work in the EST time zone (A must)
Design, operate, and troubleshoot large-scale GPU infrastructure for distributed training and inference, partnering with customers to improve cluster reliability, performance, and capacity.
Own end-to-end reliability for a customer-facing GPU cloud, from tenant onboarding and workload execution to incident response, by building the observability, automation, and operational practices that keep service dependable at scale.
Build and operate reliability, observability, and incident-management systems for regulated cloud production environments, partnering across engineering and lab teams to improve service reliability and compliance.
Lead large-scale reliability and infrastructure initiatives, architect cloud solutions, improve operational resilience through automation and incident response, and guide compliance efforts while partnering with teams and enterprise.
Own production reliability, scalability, and performance by defining SLOs and error budgets, leading incident response, improving observability and automation, and scaling secure Kubernetes-based platforms.
