Engineering Manager, Agentic GenAI Platform

NVIDIA is seeking an Engineering Manager to lead the development of an agentic platform for observing, debugging, and optimizing GenAI models deployed at scale. In this role, you will lead a team building an agentic platform that provides visibility into model behavior, inference performance, reliability, and cost across large-scale GenAI workloads. This agentic platform will capture, correlate, and analyze logs, traces, metrics, and performance signals across large scale LLM and VLM deployments. It will help engineers understand model-serving behavior, find regressions, optimize latency and throughput, and improve the reliability of GenAI systems in production.

You will work across the NVIDIA AI software stack with teams focused on inference serving, model optimization, distributed systems, GPU performance, and production operations. This is a highly cross-functional role for someone who understands deep learning systems, observability, and large-scale software platforms, and who is excited about building agentic workflows that help teams reason over complex telemetry and performance data.What You’ll Be Doing:

• Lead, mentor, and grow a team building an agentic platform for monitoring and improving large-scale LLMs and VLMs in production.

• Build systems that collect, correlate, and analyze telemetry across inference servers, GPUs, schedulers, model runtimes, and customer-facing APIs.

• Develop agentic workflows that help engineers identify root causes, explain regressions, and recommend performance optimizations.

• Collaborate with internal customers and business units to align priorities and deliver production grade platform capabilities.

What We Need To See:

• BSc, MS, or PhD in Computer Science, Computer Engineering, or equivalent experience.

• 8+ years of relevant software engineering experience, including 3+ years in engineering management or technical leadership.

• Experience leading software engineering teams building large-scale distributed systems, observability platforms, ML infrastructure, or production AI systems.

• Strong understanding of LLM/VLM inference systems, deployment patterns, and production performance challenges.

• Experience with logs, metrics, traces, profiling, alerting, dashboards, or incident/debugging workflows.

• Strong programming, debugging, performance analysis, and test design skills.

• Ability to work across organizations and align technical priorities with product and business goals.

• Excellent communication and collaboration skills.

Ways To Stand Out From The Crowd:

• Background in GPU performance analysis, distributed inference, model serving optimization, or reliability engineering.

• Experience building observability or telemetry platforms for AI, ML, cloud, or distributed infrastructure.

• Experience with OpenTelemetry, Prometheus, Grafana, Jaeger, ClickHouse, Elastic, or similar observability tools.

• Experience building agentic systems that reason over logs, traces, performance data, incidents, or operational workflows.

• Hands-on experience with production GenAI serving systems and metrics such as TTFT, TPOT, throughput, queueing delay, GPU utilization, KV cache pressure, error rates, and cost per token.

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