Lead Software Engineer – LLM Ops Platform Reliability

Help shape how AI systems run reliably in this role youll build and operate large language model serving infrastructure bringing strong engineering fundamentals and site reliability practices to cutting-edge AI platforms. Youll 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 youll find meaningful impact and growth here.

As a Lead Software Engineer at JPMorgan Chase in 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 LLM inference platform end to end. You will operate large language model serving stacks (such as vLLM and llm-d) 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
• Operate and scale LLM serving infrastructure (such as vLLM and llm-d) including model hosting request routing continuous batching and KV-cache optimization
• Deploy host and lifecycle-manage open-source and proprietary LLMs on Amazon EKS and Amazon SageMaker as well as on-prem and local GPU clusters using reproducible infrastructure as code and continuous delivery pipelines
• Implement observability (logs metrics traces) with dashboards and actionable alerting including Prometheus metrics and Grafana/Alertmanager integration for LLM and GPU workloads
• Tune GPU and accelerator capacity autoscaling and cost efficiency for LLM inference workloads using performance and optimization techniques (e.g. quantization parallelism speculative decoding)
• Lead reliability engineering for LLM endpoints through capacity planning load/soak testing safe rollouts (blue/green canary) failover and incident response for outages and model-quality regressions
• Participate in an on-call rotation lead incident triage and mitigation and produce clear post-incident root-cause analyses and follow-ups
• Identify recurring operational issues and automate remediation to improve platform stability and developer experience
• Build and maintain multi-agent systems with strong orchestration (planning coordination tool-calling state/memory and workflow control) where appropriate
• Contribute to an inclusive team culture grounded in diversity opportunity inclusion and respect and help drive adoption of leading-edge technologies through communities of practice

Required Qualifications Capabilities and Skills

• Formal training certification or equivalent practical experience in software engineering concepts
• 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 AWS and Terraform for infrastructure 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
• Comfort with on-call operations and production troubleshooting
• Hands-on production experience operating LLM inference servers such as vLLM and llm-d (or directly equivalent serving stacks)
• Hands-on experience hosting and serving LLMs on Amazon EKS and/or Amazon SageMaker and on local GPU infrastructure
• Knowledge of LLM reliability and risk considerations including latency/throughput trade-offs model and weight versioning prompt/response logging and safe rollout patterns

Preferred Qualifications Capabilities and Skills

• Experience developing generative AI applications AI agents vector search and retrieval-augmented generation patterns
• Experience building AI agents using frameworks such as LangChain CrewAI LangGraph or similar orchestration platforms
• Experience operating or integrating serving platforms such as KServe Ray Serve NVIDIA Triton Inference Server Text Generation Inference (TGI) alongside vLLM/llm-d
• Familiarity with Amazon SageMaker JumpStart SageMaker Endpoints and Amazon Bedrock for managed model hosting
• Experience with online LLM quality monitoring (e.g. hallucination toxicity drift detection) and tracing via OpenTelemetry conventions
• Contributions to open-source LLM serving or inference projects (e.g. vLLM llm-d Ray KServe Triton)