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Lead Software Engineer - AI Developer

Job in Plano, Collin County, Texas, 75086, USA
Listing for: JPMorgan Chase & Co.
Full Time position
Listed on 2026-09-01
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), DevOps, Software Engineer, Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 180000 - 250000 USD Yearly USD 180000.00 250000.00 YEAR
Job Description & How to Apply Below

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 JPMorgan

Chase 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, Open Search, pgvector, FAISS)
  • Experience with AI orchestration frameworks:
    Lang Chain, Llama Index, 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
Preferred qualifications, capabilities, and skills
  • Financial Services industry experience
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