• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problem. 
• 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.

• Own infrastructure capacity optimization solutions and build predictive/prescriptive models to identify capacity risk, performance bottlenecks, and right-sizing opportunities.

• Design, develop, and productionize GenAI/agentic AI solutions for automation, decision support, and operational workflows, including LLM/SLM apps such as RAG and summarization/extraction.

• Engineer production-grade backend services in Python/Java (REST APIs, microservices, reusable libraries) and own cloud-native data ingestion/processing pipelines for capacity analytics and AI use cases.

• Build prompt engineering assets, routing strategies, and guardrails, and implement automated plus human-in-the-loop evaluation to improve quality.

• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

• Apply MLOps best practices across experimentation, versioning, CI/CD, deployment, monitoring, and lifecycle management; implement testing/benchmarking and observability; define success metrics/governance with stakeholders; and mentor engineers to uphold high standards.

• Own and govern the end-to-end AI/ML optimization strategyfrom architecture and engineering standards (quality, lifecycle, observability, secure SDLC) through cross-functional execution with SRE/platform/businessto deliver scalable automation, risk reduction, and measurable enterprise outcomes.

• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture 

•  Experience with MCP (Model Context Protocol), Agent Skills, and structured agentic architectures.
• Strong practical usage of AI engineering productivity tooling (for example, GitHub Copilot, Claude Code) in enterprise SDLC environments.
• Familiarity with VSI and Cloud Foundry contexts.
• Advanced Java engineering proficiency in addition to Python.
• Expert-level Python for production systems (packaging, dependency management, performance); strong Java proficiency is a plus.

This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChases review of criminal conviction history, including pretrial diversions or program entries.