Lead Software Engineer - AI Developer

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Community & Consumer Bank - CX Loyalty Travel team, you are 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Serves as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software  Development Life Cycle
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies
• Adds to the team culture of opportunity, inclusion, and respect
• Ability to tackle design and functionality problems independently with little to no oversight
• 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.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience
• Advanced in programming languages Java, Python or Node.js
• Strong proficiency with RAG architectures — chunking strategies, embedding models, vector stores (Pinecone, OpenSearch, pgvector, FAISS)
• Experience with AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or CrewAI
• Hands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs
• Proven prompt engineering skills — system prompts, few-shot, chain-of-thought, tool use, structured outputs
• Experience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech integration)
• Proficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)
• Experience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code
• Strong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems
• Familiarity with evaluation frameworks for LLM outputs. Experience with guardrails, content filtering, and responsible AI practices
• 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

• Financial Services industry experience