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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.
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1138 open roles
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As the Sr Director of Software Engineering - AI/ML Solutions within Consumer & Community Banking, you will lead multiple technical areas and departments while collaborating across domains to implement advanced technical methods. You will design and implement agentic AI reference architectures involving orchestration, retrieval, memory, guardrails, and evaluation harnesses. Your daily work involves writing production-quality Python code using PyTorch or TensorFlow, building LLM-powered APIs and microservices, and managing 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 pipelines. The role requires expertise in vector storage systems, cloud-native AI deployment on AWS or Azure, and developing autonomous agents. You will establish engineering standards for agentic AI systems within a regulated financial services environment to ensure security and scalability.
What does a Software Engineering Director earn?
Median $260125 from 30 postings across 15 companies.
Design and implement agentic AI reference architectures including orchestration, retrieval, memory, and evaluation harnesses.
Write and review production-quality Python code using PyTorch or TensorFlow for critical-path systems.
Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows.
Manage the end-to-end ML lifecycle including CI/CD, monitoring, drift detection, and model versioning.
Optimize inference performance for latency, throughput, caching, and cost per inference.
Partner with data teams to develop structured and unstructured data pipelines and document ingestion systems.
Establish engineering standards and govern agentic AI-enabled workflows to improve delivery speed and code quality.
Define guardrails for safety, security, and resiliency across all AI-driven development processes.
What we're looking for
Formal training or certification on software engineering concepts and 10+ years of applied experience.
5+ years of experience leading technologists to manage and solve complex technical items within your domain.
Strong Python engineering skills with experience in PyTorch or TensorFlow.
Expertise in vector storage systems, designing memory for agents, and developing autonomous long-running agents.
Proven experience deploying LLM-backed services (APIs, microservices) and deep MLOps experience including CI/CD and model governance.
Cloud-native AI deployment experience on AWS or Azure with cost and performance optimization.
Experience designing and leading adoption of agentic AI-enabled development practices across teams.
Background operating AI systems in regulated environments (preferred); experience with prompt engineering, LLM orchestration, and mentoring senior engineers (preferred).
Agentic AI
SDLC
TLM Automation
Cloud Native
AI-assisted Development
Release Governance
Resilience Engineering
Incident Response
System Design
Software Development Life Cycle
Portfolio Governance
Security
Scalability
AWS
Java
AI-assisted development
LLMs
Agentic AI
SDLC
Data Ingestion Pipelines
Dependency Graph Modeling
Cloud Native
Software Development Life Cycle
System Design
Testing
Infrastructure
Security
Compliance
AI
Azure
Azure AI Services
Azure SQL
Azure Service Bus
Event-Driven Architecture
API Development
Prompt Engineering
GitHub Copilot
Cursor
DevOps
Domain Driven Design
HL7v2
FHIR
x12 278
SQL