Senior Lead Software Engineer - LLM Ops Platform Reliability

Help shape how AI systems run reliably in production  this role, you'll build and operate large language model serving infrastructure, bringing strong engineering fundamentals and site reliability practices to cutting-edge AI platforms. You'll work hands-on with cloud and Kubernetes-based deployments, deep observability, and cost-aware performance tuning. If you enjoy solving hard production problems and making platforms measurably better, you'll find meaningful impact and growth here.

As a Senior Lead Software Engineer at JPMorgan

Chase within the AI and Machine Learning Platform team, you will build and scale AI infrastructure that modernizes traditional infrastructure management and site reliability engineering through applied AI. You will own the reliability, performance, and cost-efficiency of the large language model inference platform end to end. You will operate large language model serving stacks in production at scale, with deep instrumentation and strong operational rigor.

You will partner across engineering to deliver secure software, improve stability, and lead incident response and continuous improvement.

Job responsibilities

Design, develop, troubleshoot, and deliver secure, high-quality production software and services for AI infrastructure

Build backend services and APIs that enable reliable operation of AI infrastructure in production environments

Operate and scale large language model serving infrastructure, including model hosting, request routing, continuous batching, and cache optimization

Deploy, host, and lifecycle-manage open-source and proprietary large language models on cloud-based container orchestration platforms and on-premises GPU clusters using reproducible infrastructure as code and continuous delivery pipelines

Implement observability across logs, metrics, and traces with dashboards and actionable alerting for large language model and GPU workloads

Tune GPU and accelerator capacity, autoscaling, and cost efficiency for large language model inference workloads using performance optimization techniques such as quantization, parallelism, and speculative decoding

Lead reliability engineering for large language model endpoints through capacity planning, load and soak testing, safe rollouts, failover, and incident response for outages and model-quality regressions

Participate in on-call rotations, lead incident triage and mitigation, and produce clear post-incident root-cause analyses and follow-up actions

Identify recurring operational issues and automate remediation to improve platform stability and developer experience

Build and maintain multi-agent systems with strong orchestration, including planning, coordination, tool-calling, state and memory management, and workflow control where appropriate

Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. Required qualifications, capabilities, and skills

Hands-on experience with system design, application development, testing, and operational stability in production environments

Advanced proficiency in Python for building production-grade services and tooling

Proficiency with automation and continuous delivery methods

Hands-on experience with cloud infrastructure platforms and infrastructure-as-code tooling for delivery and lifecycle management

Strong understanding of site reliability engineering practices, including incident management, root-cause analysis, runbooks, and reliability patterns

Practical knowledge of observability and instrumentation across metrics, logs, and traces

Hands-on experience with Kubernetes and container-based orchestration platforms, including managed cloud variants

Experience hosting and serving large language models on cloud-based infrastructure and local GPU environments

Knowledge of large language model reliability and…