Principal Applied AI ML Engineer

Testing Site - JPMC; Seattle (WA); On-site; USD 180,000 - 270,000; Full time

Job summary

JPMorgan Chase in Seattle seeks a Principal AI/ML leader to shape the Corporate Sector's AI/ML & Data Platforms. You will guide multiple ML projects, lead a team of ML and software engineers, and drive production-grade ML solutions with strong focus on operational excellence.

You will influence roadmaps, ensure responsible AI practices, and collaborate across product, risk, legal, and compliance teams while delivering scalable, low-latency ML services in the cloud.

Qualifications

• 10 years of experience building applied ML systems with recent hands-on work in LLMs or agentic AI.
• Strong Python engineering with PyTorch or TensorFlow.
• Experience deploying LLM-backed services to production (APIs, microservices).
• Deep MLOps experience including CI/CD, monitoring, incident response, and model governance.
• Cloud-native AI deployment on AWS or Azure, with cost and performance optimization.
• Ability to lead technically and influence outcomes without formal authority.
• Experience creating reusable platforms and patterns that accelerate delivery.
• Demonstrated commitment to responsible AI practices and operational excellence.
• Strong communication and collaboration skills, working across product, risk, legal, and compliance teams.
• Experience optimizing inference and ML system performance.

Responsibilities

• Design and implement agentic AI reference architectures, including orchestration, memory, guardrails, and evaluation harnesses.
• Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
• Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
• Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
• Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
• Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
• Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
• Embed responsible AI practices: safety, policy enforcement, audit logging, explainability, and monitoring
• Set engineering standards for agentic AI systems and lead design reviews
• Mentor senior engineers through code reviews and architecture discussions
• Influence roadmap and priorities through technical insight and delivery

Skills

Python

PyTorch

TensorFlow

LLMs

Agentic AI

Cloud engineering

MLOps

Mentoring

Vector storage

Distributed computing

Tools

Kubernetes

Git

REST APIs

Job description

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together.

As a Principal AI/ML at JPMorgan Chase within the Corporate Sector - AI/ML & Data Platforms, you will lead a specialized technical area, driving impact across teams, technologies, and projects. In this role, you will leverage your deep knowledge of machine learning, software engineering, and product management to spearhead multiple complex ML projects and initiatives, serving as the primary decision-maker and a catalyst for innovation and solution delivery.

You will be responsible for hiring, leading, and mentoring a team of Machine Learning and Software Engineers, focusing on best practices in ML engineering, with the goal of elevating team performance to produce high-quality, scalable ML solutions with operational excellence. You will engage deeply in technical aspects, reviewing code, mentoring engineers, troubleshooting production ML applications, and enabling new ideas through rapid prototyping. Your passion for parallel distributed computing, big data, cloud engineering, micro-services, automation, and operational excellence will be key.

Job Responsibilities

• Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses.
• Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code
• Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores
• Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows
• Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback
• Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference
• Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls
• Embed responsible AI practices: safety, policy enforcement, audit logging, explainability, and monitoring
• Set engineering standards for agentic AI systems and lead design reviews
• Mentor senior engineers through code reviews and architecture discussions
• Influence roadmap and priorities through technical insight and delivery

Required Qualifications, Capabilities, and Skills:

• 10 years of experience building applied machine learning systems, with recent hands-on work in LLMs or agentic AI
• Strong Python engineering skills; experience with PyTorch or TensorFlow
• Expertise working with Vector storage systems and designing memory for Agents
• Expertise developing long running agents that run autonomously using tools, skills and human in the loop
• Proven experience deploying LLM-backed services to production (APIs, microservices)
• Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance
• Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization
• Ability to lead technically and influence outcomes without formal authority
• Experience creating reusable platforms and patterns that accelerate delivery
• Demonstrated commitment to responsible AI practices and operational excellence
• Strong communication and collaboration skills, working across product, risk, legal, and compliance teams
• Experience optimizing inference and ML system performance

Preferred Qualifications, Capabilities, and Skills:

• Experience with fine-tuning, adapters, or custom evaluation frameworks
• Background operating AI systems in regulated environments (finance, healthcare, etc.)
• Experience with prompt engineering and LLM orchestration
• Knowledge of safety filters, audit logging, and explainability in production systems
• Experience mentoring senior engineers and leading architecture discussions
• Demonstrated ability to influence technical roadmaps and priorities

FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.