Executive Director, Machine Learning & Gen AI Platforms (Home Lending)

JPMorganChase • Plano, TX

5d

About The Position

Join a team where your ideas move from experimentation to production at enterprise scale. You’ll help shape how we build, govern, and operate modern AI platforms—working with partners across product and engineering to deliver durable, secure, and high-performing capabilities that improve customer and business outcomes. As an Executive Director at JPMorganChase within Consumer and Community Banking on the Home Lending Architecture team, you will lead innovation across machine learning platforms, Generative AI platforms, and Home Lending data platform architecture. You will work closely with product owners and software engineering teams to design end-to-end solutions, guide technical decisions, and turn prototypes into production-ready platforms. You’ll also mentor other AI engineers and help build a culture of continuous learning and architecture evolution.

Requirements

• Formal training or certification on data architecture concepts and 10+ years of applied experience
• 10+ years of experience leading technologists to anticipate, manage, and solve complex technical challenges
• Advanced proficiency in one or more programming languages (Python, Java, or C/C++), with intermediate Python required
• Hands-on experience with system design, application development, testing, and operational stability in production environments
• Advanced knowledge of software architecture and technical processes, including significant depth in cloud and machine learning technologies
• Hands-on experience with machine learning techniques, including natural language processing, large language models, and deep learning frameworks (such as PyTorch or TensorFlow)
• Applied experience in areas such as GPU optimization, fine-tuning, embedding models, inference optimization, prompt engineering, evaluation, and retrieval-augmented generation
• Practical cloud-native experience, especially with Amazon Web Services
• Experience with data engineering patterns and tools, including streaming, extract-transform-load or extract-load-transform pipelines, and analytics tooling
• Ability to independently drive design and delivery from ideation through implementation
• Strong communication skills and leadership presence, with the ability to partner effectively across engineering, product, and machine learning practitioners

Nice To Haves

• Experience with distributed training frameworks and experiment tracking tools (such as Ray and MLflow)
• Experience with embedding-based search and ranking, recommender systems, graph-based methods, or related advanced approaches
• Knowledge of reinforcement learning or meta-learning methods
• Deep understanding of large language model techniques such as agentic workflows, planning, and reasoning approaches
• Experience building and deploying machine learning solutions on Amazon Web Services using managed training and container orchestration services
• Familiarity with modern data publishing and consumption patterns across teams and platforms
• Proficiency with data architecture toolsets such as data modeling tools and cloud-based data platforms

Responsibilities

• Own and champion architecture solutions across data platforms, machine learning platforms, and Generative AI platform capabilities
• Partner with engineering teams to provide hands-on solution design support and enable reliable implementation of processes and procedures
• Represent product areas in architecture governance forums, driving accountability for code-level decisions, control obligations, cost of ownership, maintainability, and operational outcomes
• Evaluate current technology and lead assessments of new technologies using established standards and frameworks
• Serve as a subject matter expert across a wide range of machine learning techniques and optimizations, including distributed deployment, training, and serving
• Design and implement Generative AI workflows using large language models, including evaluation methods and feedback loops for model and pipeline improvement
• Translate experimental results into production-ready solutions by collaborating closely with engineering teams across the delivery lifecycle
• Improve accuracy, latency, and reliability by identifying bottlenecks and driving performance and scalability optimizations
• Collaborate with product and engineering to deliver tailored, science- and technology-driven solutions that meet clear business needs
• Influence product design and technical operating models by advocating for leading-edge technologies and practical, secure adoption patterns

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: No Education Listed