Lead Software Engineer AI Platforms

GB Intelligence is transforming how Global Banking gets work donebringing AI-driven insights and workflow automation into the heart of deal origination market and sector intelligence and client coverage.

As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Banking - Global Banking Technology team youare an integral part of an agile team that works to enhance build and deliver trusted market-leading technology products in a secure stable and scalable way. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.

You will help deliver high-impact production-grade capabilities: building scalable and resilient services engineering secure data flows and integrating seamlessly with the tools bankers rely on every day (CRM market data platforms modeling environments and document/compliance systems). Youll turn complex client deal and market data into trusted insights and outputsthen operationalize them across downstream systems to accelerate execution strengthen risk discipline and elevate the day-to-day experience for Global Banking teams.

Job responsibilities

• Design and implement complex software components across backend services APIs and UI experiences using Java Python and React applying sound engineering judgment and pragmatic architecture.
• Build and refine agentic capabilities using the Smart SDK including tool integration orchestration patterns and safety/reliability guardrails suitable for production use.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality delivery speed and operational outcomes (e.g. AI-assisted code review/refactoring test strategy acceleration incident/root-cause analysis support) while establishing consistent validation standards (secure coding peer review automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Develop and optimize RAG pipelines end-to-end (ingestion chunking embeddings retrieval reranking prompt/response patterns) improving relevance latency and robustness with OpenSearch and continuous measurement.
• Write secure high-quality production code and raise the bar through code reviews debugging and hands-on mentorshipimproving 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 PostgreSQL (schema design migrations query tuning) and OpenSearch (indexing strategies 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.
• Participate in technical evaluation sessions with internal partners and external vendorsassessing architecture technical depth and fit within existing platforms and information architecture.
• Champion modern engineering practices and knowledge-sharing contributing to communities of practice and accelerating adoption of leading-edge technologies.
  

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience
• Strong hands-on expertise in Java/J2EE Spring Boot and microservices architecture building secure high-quality production-grade systems.
• Proficiency with AWS Terraform GitHub Jenkins and modern developer tooling (e.g. GitHub Copilot).
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g. for coding code review test acceleration troubleshooting) with the ability to set team expectations for validating AI outputs for correctness performance and security.
• Strong understanding of responsible AI use in engineering workflows including data sensitivity considerations secure handling of inputs/outputs and adherence to resiliency and security expectations; experience coaching engineers on safe compliant adoption within delivery practices
• Databases: proficiency with relational databases (e.g. PostgreSQL MySQL) NoSQL databases (e.g. DynamoDB Redis etc.) and GraphQL.
• Containerization: experience with Docker and container orchestration (ECS EKS or Kubernetes).
• Demonstrated experience developing debugging and maintaining software in a large corporate environment using one or more modern programming languages and database querying languages.
• Demonstrable ability to write high-quality code in one or more languages with strong debugging and troubleshooting skills.
• Emerging knowledge of software applications and technical processes within a technical discipline (e.g. cloud artificial intelligence machine learning mobile etc.).
• Demonstrated expertise with monitoring/observability tools (e.g. Splunk Datadog Dynatrace CloudWatch) and proven capability to lead high-performing teams by influencedriving innovation maintaining strong team health and owning the end-to-end performance cycle
  

• Experience across the full Software Development Life Cycle (SDLC) from design and implementation through testing deployment and production support.
• Exposure to agile engineering practices including CI/CD application resiliency and secure engineering.