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.
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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.
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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.
- 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.
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Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
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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 and effective usage within team practices.
- Comfort working with LLM-based systems (prompting patterns, structured outputs, evaluation, and guardrails) in real-world environments.
- A high learning mindset—curious, adaptable, and motivated to apply modern best practices thoughtfully rather than chasing hype.
- Ability to collaborate across engineering, product, and risk/compliance partners to deliver safely and responsibly.
Preferred qualifications, capabilities, and skills
- Knowledge of generative AI concepts., prompt engineering, RAG, embeddings, and vector search, LangChain
- Knowledge of model evaluation, guardrails, hallucination reduction
- Experience with Python, Java(spring boot),Open AI, Database(vector/pinecone), AWS
- Knowledge on open source community contribution/start up experience is plus
- Self motivation and Passion is the key driver