Lead Software Engineer - LLM Ops Platform Reliability

Hackajob Ltd; Paisley; On-site; GBP 120,000 - 180,000; Full time

Job summary

JPMorgan Chase is seeking a Lead Software Engineer for the AI and Machine Learning Platform team to design and operate scalable AI infrastructure. You will own reliability, performance, and cost-efficiency of the LLM inference platform end-to-end, deploying on Amazon EKS, SageMaker, and on-prem GPU clusters with strong observability.

You will lead incident response, capacity planning, and continuous improvement while collaborating across engineering to deliver secure software and robust

Qualifications

• 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.
• Hands-on production experience operating LLM inference servers and related tooling.

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 and model hosting on cloud and on-prem clusters.

Skills

Python

AWS

Kubernetes

Terraform

Site reliability engineering

Observability

Incident management

Tools

Terraform

AWS

Kubernetes

Job description

hackajob is partnering directly with JPMorganChase to hire for this role.

JOB DESCRIPTION

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 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)

ABOUT US

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

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ABOUT THE TEAM

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.