Senior Director of Software Engineering, AI/ML Solutions

JPMorgan Chase

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: Plano, TX
Employment: Full-time
Posted: 49 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1239 open roles on FindRole.

Listed pay typically runs $136,000–$207,500 across 6 roles with salary data.

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TL;DR · Senior Director of Software Engineering, AI/ML Solutions

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JOB TITLE: Sr Director of Software Engineering - AI/ML Solutions As the Sr Director of Software Engineering - AI/ML Solutions within Consumer & Community Banking, you will lead multiple technical areas and departments to drive the adoption of technical methods across various teams. You will design and implement agentic AI reference architectures, including orchestration, retrieval, memory, and guardrails. Your daily work involves writing production-quality Python code using PyTorch or TensorFlow, building LLM-powered APIs and microservices, and owning the end-to-end ML lifecycle including CI/CD, monitoring, and drift detection. You will optimize inference for latency and cost while partnering with data teams on structured and unstructured data pipelines. Key technical requirements include expertise in vector storage systems, cloud-native AI deployment on AWS or Azure, and establishing engineering standards for responsible AI practices in regulated environments.

Skills

Python PyTorch TensorFlow LLM MLOps CI/CD AWS Azure Microservices APIs Agentic AI Prompt Engineering Data Pipelines Monitoring Automated Testing Model Governance

What you'll do

Design and implement agentic AI reference architectures including orchestration, retrieval, memory, and guardrails.
Write and review production-quality Python code using PyTorch or TensorFlow.
Build and operate LLM-powered APIs and microservices for advisor and client workflows.
Manage the end-to-end ML lifecycle including CI/CD, monitoring, drift detection, and versioning.
Optimize inference for latency, throughput, caching, batching, and cost per inference.
Partner with data teams to build structured and unstructured data pipelines and document ingestion.
Set engineering standards for agentic AI systems and lead technical design reviews.
Architect AI-enabled engineering workflows to improve delivery speed, code quality, and operational outcomes.

What we're looking for

Formal training or certification in software engineering concepts and 10+ years of applied experience.
5+ years of experience leading technologists to manage and solve complex technical items within a domain of expertise.
Strong Python engineering skills with experience using PyTorch or TensorFlow.
Expertise in vector storage systems, designing memory for agents, and developing long-running autonomous agents.
Proven experience deploying LLM-backed services to production and deep MLOps experience including CI/CD and model governance.
Cloud-native AI deployment experience on AWS or Azure with cost and performance optimization.
Demonstrated experience designing and leading the adoption of agentic AI-enabled development practices across teams.
Background operating AI systems in regulated environments (preferred).

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