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Lead Software Engineer on JPMorgan Chase's Commercial & Community Banking team in Plano, TX, designing full-stack, cloud-native Java microservices on AWS (ECS/EKS, Postgres) and building LLM/AI-driven capabilities and AI-assisted engineering practices into business workflows.
JPMorgan Chase is hiring an Applied AI/ML Lead on its Corporate Oversight and Governance data science team, based at its Hudson County office. The role designs and delivers secure, stable, and scalable AI/ML-driven technology solutions across corporate technology business functions.
Hands-on AI/ML lead engineer (VP) on JPMorgan's Asset & Wealth Management AI Engineering team, building and productionizing machine learning and LLM-powered applications for Private Bank clients. Day-to-day spans end-to-end solution design, Python development, LLM tooling (tracing, evals, guardrails), data pipelines, and cloud deployment on AWS/Azure.
A senior analytics role on JPMorgan Chase's MLIO team, optimizing GenAI/LLM-powered products (Conversational Insights, Insights for Coaching) across AWS and on-prem environments. Day-to-day involves SQL/Python data analysis, causal inference and ML-based root cause analysis, and presenting insights to leadership using tools like Tableau, Alteryx, Snowflake, and Spark.
Lead engineer for JPMorganChase's internal AI Agent Platform: designing and delivering the SDK, agent orchestration, RAG/NL2SQL capabilities, voice and document-extraction agents, and agentic memory in Python, while setting engineering standards for teams building LLM-powered agents across the firm.
JPMorgan Chase is hiring a Senior Lead Software Engineer on its Consumer & Community Banking AI/ML Platform Engineering team to build, scale, and maintain machine learning platforms — model training, deployment, monitoring infrastructure, APIs, and CI/CD pipelines. Core stack: Python, TensorFlow/PyTorch, Spark/SQL, cloud ML platforms, and MLOps tooling.
Senior Lead Software Engineer on JPMorgan Chase's Commercial & Investment Bank 'AI for Technology' team, leading a local team that applies AI (LLMs, generative AI, coding assistants) to software engineering workflows. Day to day: writing secure Java/Python production code, driving AI-assisted engineering adoption, and productionizing AI/ML solutions.
Senior technical leader on JPMorgan Chase's Commercial & Investment Banking Payments data analytics team, building and running NEO — the firm's agent runtime platform for payments technology. Sets the architecture for how ML-driven agents execute, communicate, and maintain memory.
Lead hands-on ML engineering of NEO, JPMorgan Chase's agent runtime platform for Payments Technology, building major runtime components like secure execution and agent-to-agent communication on the Commercial & Investment Banking Data Analytics Payments agile team.
Lead the development of secure and scalable technology products for Risk Technology, focusing on data pipelines and ML/AI integration using Python and PySpark in an agile environment.
Lead Software Engineer on JPMorgan Chase's Corporate Technology Instrument Reference Data team, designing and delivering secure, scalable Java/Spring Boot microservices on AWS and building batch and real-time ML systems, while driving enterprise AI-assisted engineering practices across the team.
Summer associate (intern) machine learning engineer on JPMorgan's Asset & Wealth Management AI Engineering team, building LLM and generative AI applications for the Private Bank — developing models, evaluations, data pipelines, and cloud inference services. Core stack: Python, NLP/LLMs, ML libraries, and cloud platforms.
Lead Software Engineer at JPMorgan Chase's CCB AI/ML engineering team in Manhattan, acting as a core technical contributor who designs, builds, and secures AI/ML production code in Java/Python on AWS, while driving adoption of AI-assisted engineering practices, automation, and operational stability across an agile team.
Senior AI/ML engineer on JPMorgan's Asset & Wealth Management AI Engineering team in Jersey City, NJ: hands-on design, development, and production deployment of ML/LLM-powered business solutions, scalable data pipelines, and cloud inference services (AWS/Azure), primarily in Python.
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