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Sr Lead Software Engineer

Job in City Of London, Central London, Greater London, England, UK
Listing for: JPMorgan Chase & Co.
Full Time position
Listed on 2026-07-16
Job specializations:
  • Software Development
    DevOps, AI Engineer (Applied/Software), Software Engineer, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 110000 - 140000 GBP Yearly GBP 110000.00 140000.00 YEAR
Job Description & How to Apply Below
Location: City Of London

Key Responsibilities of the Role

  • Design and build AI-augmented migration tooling, using Claude Code and Copilot, that automates discovery, code transformation, containerisation, and validation across compute platforms.
  • Engineer agentic workflows that analyse legacy workloads, generate migration artefacts (Dockerfiles, Helm/Kubernetes manifests, CI/CD pipeline definitions), and produce reviewable pull requests against real codebases.
  • Build the guardrails: automated validation, rollback, and continuous verification so AI-generated migration changes are safe to ship at scale.
  • Establish reusable patterns, prompts, evals, and reference implementations that let the rest of the migration org apply these tools consistently and reliably.
  • Partner closely with the platform engineering teams that build and operate GKP, GCS, Gaia VSI and the container golden path, so the tooling targets the correct end state.
  • Work directly with the migration execution and enablement teams to understand real blockers, then encode the solutions into tooling rather than one-off fixes.
  • Measure and improve the quality, cost, and throughput of AI-driven migration — treating model output quality and human-review load as engineering metrics to optimise.
  • Contribute to the firm s practice for safe, effective use of agentic coding tools on production codebases.
Attributes of Engineers in the Platform Migration group
  • A builder s bias: you ship tools that other engineers depend on, and you measure success by migrations completed, not demos given.
  • Comfort at the frontier: you are energised, not intimidated, by fast-moving AI tooling and are willing to establish practice where none exists yet.
  • Healthy scepticism: you trust automated output only as far as your validation proves it, and you build the checks accordingly.
  • Optimism and adaptability when faced with legacy complexity, coupled with the drive to solve hard problems and continuously optimise.
  • Respect for people and opinions, and the confidence to offer your point of view.
  • Dedication to continuous improvement of your own skillset and of the tools around you.
  • A strong personal identification with the firm s values.
Required qualifications, capabilities, and skills
  • Strong software engineering fundamentals and hands-on delivery in Python, Go, or Java.
  • Practical, production-grade use of AI coding assistants - Claude Code, Git Hub Copilot, or equivalent agentic tooling - to build and ship real software, not just autocomplete.
  • Building automation and tooling that operates on real codebases: code parsing/transformation, templating, and generating change as reviewable pull requests.
  • Cloud-native platforms and their primitives:
    Kubernetes, containers (Docker/OCI), and at least one of AWS, GCP, or Cloud Foundry / VCF.
  • CI/CD and automated deployment pipelines.
  • Designing validation and guardrails for automated change, testing, verification, and safe rollback.
  • End-to-end application infrastructure concerns such as authentication/authorization and systems integration.
  • A consultative, problem-solving approach and the ability to communicate technical concepts clearly.
  • Excellent written and spoken communication skills.
  • Bachelor s degree in Computer Science, Computer Engineering, or a related field of study, plus working experience in a role such as Software Engineer, Application Developer, or related occupation.
Preferred qualifications, capabilities, and skills
  • Experience building on top of LLM APIs: agent frameworks, tool/function calling, retrieval, and writing evals to measure output quality.
  • Prompt and context engineering as an applied discipline, including cost/latency/quality trade-offs.
  • Container build and supply-chain tooling:
    Dockerfiles, buildpacks/Kaniko, SBOM, image signing, hardened base images.
  • Infrastructure-as-code tools such as Hashi Corp Terraform.
  • Static analysis, AST-level code transformation, or compiler/language-tooling experience.
  • Migration or modernisation programmes at scale, and proficiency managing large infrastructure deployments (compute, container systems, storage, networking).
  • Global financial services and regulatory / compliance considerations relevant to workload deployment.
  • Database and…
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