Lead the design and delivery of platform standards and tooling (CLI, SDKs, libraries, templates, and automated checks) to improve adoption.
Engineer and operate production LLM inference services using serving engines such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent.
Drive Kubernetes deployment patterns, scaling, networking, and troubleshooting to support reliable platform operations.
Optimize inference performance by understanding GPU memory behavior, cache sizing, memory bandwidth trade-offs, and compute bottlenecks.
Evaluate inference-time quantization approaches, balancing latency, throughput, cost, and output quality for real-world workloads.

Key requirements

Lead the design and delivery of platform standards and tooling (CLI, SDKs, libraries, templates, and automated checks) to improve adoption.
Engineer and operate production LLM inference services using serving engines such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent.
Drive Kubernetes deployment patterns, scaling, networking, and troubleshooting to support reliable platform operations.
Optimize inference performance by understanding GPU memory behavior, cache sizing, memory bandwidth trade-offs, and compute bottlenecks.
Evaluate inference-time quantization approaches, balancing latency, throughput, cost, and output quality for real-world workloads.

Jobs at J.P. Morgan

6 active roles from this employer in the JobMatcher catalog.

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Jobs at J.P. Morgan

6 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Based in Glasgow, United Kingdom, J.P. Morgan invites applications for a Senior Lead Software Engineer in Corporate Technology – AI, Machine Learning and Data Platform. You will lead the design and delivery of secure, scalable platform capabilities to simplify adoption and day-to-day use, with a focus on production-grade large language model inference and Kubernetes-based deployment patterns. You will set technical direction for tooling and runtimes, mentor engineers, and promote inclusive, high-accountability practices.

Responsibilities

Lead the design and delivery of platform standards and tooling (CLI, SDKs, libraries, templates, and automated checks) to improve adoption.
Engineer and operate production LLM inference services using serving engines such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent.
Drive Kubernetes deployment patterns, scaling, networking, and troubleshooting to support reliable platform operations.
Optimize inference performance by understanding GPU memory behavior, cache sizing, memory bandwidth trade-offs, and compute bottlenecks.
Evaluate inference-time quantization approaches, balancing latency, throughput, cost, and output quality for real-world workloads.
Implement secure, high-quality production code and automation that strengthen resiliency, observability, and operational readiness.
Establish and maintain architecture and design artifacts, ensuring non-functional requirements are enforced through implementation and automation.
Drive adoption of enterprise-authorized AI-assisted engineering practices to improve code qualit

About the role

Based in Glasgow, United Kingdom, J.P. Morgan invites applications for a Senior Lead Software Engineer in Corporate Technology – AI, Machine Learning and Data Platform. You will lead the design and delivery of secure, scalable platform capabilities to simplify adoption and day-to-day use, with a focus on production-grade large language model inference and Kubernetes-based deployment patterns. You will set technical direction for tooling and runtimes, mentor engineers, and promote inclusive, high-accountability practices.

Responsibilities

Lead the design and delivery of platform standards and tooling (CLI, SDKs, libraries, templates, and automated checks) to improve adoption.
Engineer and operate production LLM inference services using serving engines such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent.
Drive Kubernetes deployment patterns, scaling, networking, and troubleshooting to support reliable platform operations.
Optimize inference performance by understanding GPU memory behavior, cache sizing, memory bandwidth trade-offs, and compute bottlenecks.
Evaluate inference-time quantization approaches, balancing latency, throughput, cost, and output quality for real-world workloads.
Implement secure, high-quality production code and automation that strengthen resiliency, observability, and operational readiness.
Establish and maintain architecture and design artifacts, ensuring non-functional requirements are enforced through implementation and automation.
Drive adoption of enterprise-authorized AI-assisted engineering practices to improve code qualit

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