Lead AI Platform Engineer – Cloud, Microservices & LLMs

Energy Jobline ZR; Glasgow; On-site; GBP 90,000 - 150,000; Full time

Job summary

JPMorganChase is seeking a Lead Software Engineer to own end-to-end AI-powered software initiatives, designing and building intelligent systems with large models, APIs, and multi-cloud deployments. You will mentor engineers, drive engineering standards, and ensure secure, scalable solutions across cloud environments.

You will work across REST/gRPC APIs, AI toolchains, and data design, with a focus on reliability, performance, and cost management in production systems.

Qualifications

• Formal training or certification on software engineering concepts and advanced applied experience.
• Proven track record leading software delivery end-to-end with strong ownership and the ability to execute independently across the full development lifecycle.
• Strong Python software engineering skills for building production-grade services and automation, with solid testing, packaging, and maintainability practices.
• Strong understanding of relational databases and SQL, including schema design, query optimization, indexing, and transaction management.
• Demonstrated experience building AI solutions using large models in production environments, including quality assurance, safety controls, evaluation, observability, and cost management.
• Strong API and microservices engineering experience, including service design, contract definition, security patterns, performance tuning, and distributed system observability.
• Hands-on multi-cloud experience (AWS ) with strong distributed systems fundamentals and a portability-minded approach to design.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment 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 practicests.
• Experience designing and building agentic architectures, including tool use, planning and execution loops, and multi-step workflow orchestration.
• Experience building reusable internal libraries or frameworks that accelerate team delivery and promote engineering consistency.
• Familiarity with advanced LLM evaluation techniques, including automated benchmarking, red-teaming, and latency/cost profiling in production.
• Experience with gRPC and Protobuf-based service design in distributed, high-throughput environments.
• Exposure to platform or developer experience engineering, including internal tooling, shared infrastructure patterns, or delivery enablement frameworks.

Responsibilities

• Lead initiatives end-to-end - from requirements clarification and architecture through implementation, testing, release, and production support - with strong ownership and minimal supervision.
• Design and implement AI solutions using large models and modern agent patterns, including prompting strategies, tool/function calling, retrieval patterns, routing, and memory/state management where applicable.
• Build guardrails, evaluation frameworks, monitoring pipelines, and cost/latency optimizations for production LLM-based systems.
• Design, build, and operate REST and gRPC APIs and microservices, defining clear contracts using OpenAPI and Protobuf while ensuring backward compatibility, authentication, rate limiting, and observability.
• Apply resilience engineering patterns - including timeouts, retries, and circuit breakers - to ensure reliable, production-grade service behavior.
• Build and maintain well-tested, maintainable Python services and automation with clear packaging, dependency management, and architectural standards.
• Own data design and implementation, including schema design, data access patterns, and complex SQL optimization aligned to performance and reliability requirements.
• Build and manage infrastructure as code using Terraform, supporting containerized deployments via Kubernetes and CI/CD pipelines across multi-cloud environments.
• Drive engineering excellence across code quality, testing strategy, performance, reliability, and operational rigor, including leading root-cause analysis for complex production issues.
• Mentor engineers, provide technical guidance, and establish standards for delivery and engineering practices across the team.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automations.

Skills

Python

API design

Microservices

Cloud platforms

Terraform

Kubernetes

gRPC

Protobuf

SQL

DevOps

AI/ML integration

Education

Software engineering degree

Tools

Terraform

Kubernetes

OpenAPI/Protobuf

Job description

JPMorganChase is seeking a Lead Software Engineer to own end-to-end AI-powered software initiatives, designing and building intelligent systems with large models, APIs, and multi-cloud deployments. You will mentor engineers, drive engineering standards, and ensure secure, scalable solutions across cloud environments.

You will work across REST/gRPC APIs, AI toolchains, and data design, with a focus on reliability, performance, and cost management in production systems.