Lead Software Engineer - LLM Ops Platform Reliability

JPMorgan Chase · Glasgow

Help shape how AI systems run reliably in production at scale. In 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 JPMorganChase 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 risk considerations, including latency and throughput trade-offs, model versioning, prompt and response logging, and safe rollout patterns
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred qualifications, capabilities, and skills

Experience operating large language model inference servers such as vLLM and llm-d (or directly equivalent model serving stacks) in production
Experience developing generative AI applications, AI agents, vector search, and retrieval-augmented generation patterns
Experience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms
Experience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks
Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hosting
Experience with online large language model quality monitoring, including hallucination detection, toxicity filtering, and drift detection using open telemetry conventions
Contributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton)

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Build and run production LLM infrastructure at scale, improving reliability, performance, and cost efficiency with applied AI.

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