Job description

As a Senior Director of Software Engineering at JPMorgan Chase within the Corporate Sector - AIML Data Platforms, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances. Job responsibilities • Leads multiple technology and process implementations across departments to achieve firmwide technology objectives • Directly manages multiple areas with strategic transactional focus • Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business • Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives • Manages multiple stakeholders, complex projects, and large cross-product collaborations • Influences peer leaders and senior stakeholders across the business, product, and technology teams • Champions the firm’s culture of diversity, equity, inclusion, and respect • Lead the development of AI/ML-centered EKS, Ray, EMR, Notebooks, and Studios that are tailored towards data scientists and ML/AI engineers. • Act as a primary engineering leader with third-party stakeholders and internal stakeholders. • Elevate the overall engineering culture and practices across the organization and the firm.Represent the organization through external and internal talks and conferences. Required qualifications, capabilities, and skills • Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise • Experience developing or leading large or cross-functional teams of technologists • Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale • Experience leading complex projects supporting system design, testing, and operational stability • Experience with hiring, developing, and recognizing talent • Extensive practical cloud native experience • Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field • Bachelor's degree in Computer Science or equivalent practical experience. • 5 years of experience working with Machine Learning, AI, Large Language Models (LLM) infrastructure, especially with training, model development, and other requests.Experience operating in a hyper-growth entrepreneurial environment, scaling new and emerging products from the ground up. Preferred qualifications, capabilities, and skills • Experience working at code level • M.Sc or Ph.D in CS or any related field. • Expertise in Machine Learning frameworks like Python, Scikit-learn, PyTorch, TensorFlow, Apache Spark, and experience with Generative AI models like GANs, Transformers, and Diffusion Models. • Knowledge of MLOps best practices around model training, evaluation, deployment, and governance. Ability to communicate and help clients operationally set up MLOps functions. • Experience integrating ML solutions with cloud platforms like AWS SageMaker, GCP Vertex AI, Azure Cognitive Services, and leveraging their pre-built capabilities. • Proficiency in ML workflow tools like Kubeflow and MLflow for experiment tracking, model management, and model serving. • Ability to identify and articulate the business value of AI/ML to stakeholders using innovative techniques like AI Readers, AI Assistants, Agentic frameworks, etc. • Strong communication, presentation, and storytelling skills to influence technical sales cycles with business decision-makers. • Passion for AI/ML and ability to stay updated on the latest advancements through conferences, publications, cohorts, etc. Experience in consulting, sales engineering, and/or customer success. • Comfortable in evangelizing and marketing ML/AI practice with internal teams, partners, and customers alike.

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