Live opening · Posted 4 days ago
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As a Software Engineer III at JPMorganChase within the Commercial and Investment Bank, you serve as a full-stack software engineer who’s passionate about building AI-powered solutions that perform reliably at enterprise scale. You’ll help shape and deliver production-grade platforms that blend strong software engineering with modern GenAI patterns—retrieval, knowledge libraries, embedding-based search pipelines, and validation frameworks—while operating in a high-compliance environment.
Job Responsibilities
Build and own end-to-end, full-stack features (UI, APIs, services, data layers) for AI-enabled products used at scale.
Design and implement knowledge library + retrieval (RAG) capabilities, including embedding generation, indexing strategies, and semantic search pipelines.
Develop AI content and document-generation solutions, with guardrails and auditability appropriate for regulated workflows.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.
Engineer agentic workflows (tool-using/step-based systems) with robust agentic validations (policy checks, workflow constraints, and deterministic controls).
Implement semantic validations to improve output quality (grounding checks, relevance, duplication detection, hallucination-reduction patterns, and structured evaluation signals).
Stay current on industry trends in applied AI/ML systems and translate them into pragmatic, maintainable engineering decisions.
Raise the bar on engineering best practices: testing strategy, reliability, observability, performance, and secure coding patterns.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Strong full-stack engineering fundamentals and a “build it right” mindset (clean APIs, robust services, thoughtful UX).
Experience building production systems: CI/CD, monitoring/alerting, incident hygiene, performance tuning, and scalability.
Familiarity with search and retrieval systems (semantic search, vector stores, indexing, ranking, query pipelines) and how they integrate into applications.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, tro
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