Senior Lead AI Engineer — Python, AWS, LLMs for Finance

JPMorgan Chase & Co.; Scotland; On-site; GBP 110,000 - 160,000; Full time

Job summary

JPMorganChase GB Intelligence is seeking a Senior Lead Software Engineer to build production-grade AI capabilities that integrate into bankers' daily workflows, delivering secure services and scalable data flows. You will lead backend design, AI tooling integration, and RAG pipelines, with OpenSearch, AWS, Terraform, and container orchestration, driving reliability, safety, and business impact.

This role requires hands-on coding, mentorship, and collaboration with enterprise teams to

Qualifications

• Formal training or certification on software engineering concepts and advanced applied experience.
• Strong hands-on expertise in Python, building production backend systems including API and service development, testing practices, performance debugging, and dependency management.
• Demonstrated experience building agentic and LLM-powered capabilities, including tool integration, orchestration patterns, and production-grade safety and reliability guardrails.
• Experience implementing evaluation and measurement loops for LLM and agent quality, including relevance and grounding checks, regression testing, and reliability and latency monitoring.
• Experience across the full Software Development Life Cycle (SDLC), from design and implementation through testing, deployment, and production support.
• Strong written and verbal communication skills, with the ability to explain technical tradeoffs clearly to cross-functional partners.
• Proficiency with AWS, Terraform, GitHub, Jenkins, and modern developer tooling.
• Experience with containerization, including Docker and container orchestration platforms such as ECS, EKS, or Kubernetes.
• Demonstrated expertise with monitoring and observability tools such as Splunk, Datadog, Dynatrace, or CloudWatch, with proven ability to influence high-performing teams by driving innovation.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment with demonstrated ability to validate AI 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 usage within team practices.

Responsibilities

• Design and implement complex backend services and APIs in Python, applying sound engineering judgment and pragmatic architecture decisions that scale across production environments
• Build and refine agentic capabilities using the Smart SDK, including tool integration, orchestration patterns, and safety and reliability guardrails suitable for production use
• Develop and optimize retrieval-augmented generation (RAG) pipelines end-to-end—covering ingestion, chunking, embeddings, retrieval, reranking, and prompt/response patterns—improving relevance, latency, and robustness with OpenSearch and continuous measurement
• Implement and maintain large language model (LLM) and agent evaluation and monitoring frameworks, including offline test sets, automated regression checks, relevance and grounding scoring, and production dashboards to continuously improve quality, safety, latency, and cost
• Engineer secure, reliable integrations with systems bankers rely on daily, ensuring traceability and operational readiness across the platform
• Write secure, high-quality production code and raise the bar through code reviews, debugging, and hands-on mentorship—improving maintainability, performance, and consistency across the codebase
• Drive operational excellence by identifying recurring issues and implementing automation, preventative controls, and reliability improvements to reduce toil and improve system stability
• Engineer data and search solutions using AWS Aurora and OpenSearch—including schema design, migrations, query tuning, indexing strategies, and query relevance tuning—to support AI and analytics workflows
• Contribute to cloud-native engineering on AWS, partnering on infrastructure-as-code with Terraform and improving deployment safety, environment consistency, and observability
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity, while validating outputs through peer review, automated testing, and secure coding 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 automation

Skills

Python

LLM capabilities

Agentic architecture

SDLC

Communication

Tools

AWS

Terraform

GitHub

Jenkins

Docker

Kubernetes

OpenSearch

Observability tools (Splunk/Datadog)

Job description

JPMorganChase GB Intelligence is seeking a Senior Lead Software Engineer to build production-grade AI capabilities that integrate into bankers' daily workflows, delivering secure services and scalable data flows. You will lead backend design, AI tooling integration, and RAG pipelines, with OpenSearch, AWS, Terraform, and container orchestration, driving reliability, safety, and business impact.

This role requires hands-on coding, mentorship, and collaboration with enterprise teams to