Executive Director, Machine Learning & Gen AI Platforms

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Plano, TX
Posted: 36 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $239k

$185k most similar roles pay here $292k

This listing doesn't post a salary. Most similar roles pay $209,775–$268,500.

Based on 240 similar postings.

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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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Executive Director, Machine Learning & Gen AI Platforms

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Executive Director, Machine Learning & Gen AI Platforms (Home Lending) leads innovation for machine learning, Generative AI, and Home Lending data platform architectures. Working within the Consumer and Community Banking team, this leader designs end-to-end solutions, translates experimental results into production-ready systems, and manages architecture governance to ensure reliability and scalability. The role involves designing Generative AI workflows using large language models, optimizing performance for accuracy and latency, and mentoring engineers. Key technical requirements include proficiency in Python, Java, or C/C++, along with expertise in PyTorch, TensorFlow, and Amazon Web Services. Candidates must possess experience in natural language processing, deep learning, GPU optimization, prompt engineering, and retrieval-augmented generation. The role addresses the challenge of building durable, secure platforms for home lending by integrating advanced machine learning techniques into stable production environments.

Skills

Generative AI Machine Learning Python Java C++ PyTorch TensorFlow Large Language Models Deep Learning Data Architecture Prompt Engineering Ray MLflow Distributed Training System Design

What you'll do

Own and champion architectural solutions for data, machine learning, and Generative AI platforms.
Provide hands-on solution design support to engineering teams to ensure reliable implementation of processes.
Represent product areas in architecture governance forums to drive accountability for code decisions and operational outcomes.
Evaluate current technologies and lead assessments of new technologies using established standards and frameworks.
Serve as a subject matter expert on machine learning techniques, including distributed deployment, training, and serving.
Design and implement Generative AI workflows using large language models with integrated evaluation methods.
Translate experimental results into production-ready solutions across the entire delivery lifecycle.
Identify bottlenecks to improve accuracy, latency, and reliability through performance and scalability optimizations.

What we're looking for

Formal training or certification in data architecture concepts and 10+ years of applied experience.
10+ years of experience leading technologists to manage and solve complex technical challenges.
Advanced proficiency in Python, Java, or C/C++, with intermediate Python required.
Hands-on experience with system design, application development, testing, and production environment stability.
Advanced knowledge of software architecture, cloud technologies, and machine learning techniques including NLP and deep learning frameworks.
Applied experience in GPU optimization, fine-tuning, embedding models, inference optimization, prompt engineering, evaluation, and RAG.
Practical cloud-native experience, especially with Amazon Web Services.
Experience with data engineering patterns, streaming, ETL/ELT pipelines, and analytics tooling.
Experience with distributed training frameworks, experiment tracking tools, or advanced search and ranking methods (preferred).
Knowledge of reinforcement learning, meta-learning, or agentic workflows for large language models (preferred).
Experience building and deploying machine learning solutions on AWS using managed training and container orchestration services (preferred).
Proficiency with data modeling tools and cloud-based data platforms (preferred).

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