Site Reliability Software Engineer Lead in 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.

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.

• 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.
• 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.
• Tune GPU and accelerator capacity, autoscaling, and cost efficiency for LLM inference workloads using performance and optimization techniques.
• 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 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.

Formal training, certification, or equivalent practical experience in software engineering concepts is required. Hands-on experience with system design, application development, testing, and operational stability in production environments is essential.

Advanced proficiency in Python for building production-grade services and tooling is necessary. Hands-on experience with AWS and Terraform for infrastructure delivery and lifecycle management is also required.

Strong understanding of site reliability engineering practices, including incident management, root-cause analysis, runbooks, and reliability patterns is important. Hands-on production experience operating LLM inference servers such as vLLM and llm-d is needed.

Knowledge of LLM reliability and risk considerations, including latency/throughput trade-offs, model and weight versioning, prompt/response logging, and safe rollout patterns is beneficial.

• 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.
• Experience with online LLM quality monitoring.
• Contributions to open-source LLM serving or inference projects.