Applied AI ML Director, Chief Data

Join our AIML Data Platforms team and shape the future of machine learning at a global financial leader. Drive innovation and deliver impactful AI solutions across diverse technologies. Lead and mentor top talent in a collaborative, growth-focused environment. Work on cutting-edge projects that transform client experiences. Make your mark in a team committed to technical excellence and operational success.

As a Director of Machine Learning Engineering at JPMorgan Chase within the Corporate Sector – AIML 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

• Lead and manage a team of machine learning engineers, ensuring the implementation, delivery, and support of high-quality ML solutions.
• Collaborate with product teams to deliver tailored, AI/ML-driven technology solutions.
• Architect and implement distributed AI/ML infrastructure, including inference, training, scheduling, orchestration, and storage.
• Develop advanced monitoring and management tools for high reliability and scalability in AI/ML systems.
• Optimize AI/ML system performance by identifying and resolving inefficiencies and bottlenecks.
• Drive the adoption and execution of AI/ML Platform tools across various teams.
• Integrate Generative AI and Classical AI within the ML Platform using state-of-the-art techniques.
• Lead the entire AI/ML product life cycle through planning, execution, and future development by continuously adapting, developing new AI/ML products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth.
• Manage, mentor, and develop a team of AI/ML professionals in a way that promotes a culture of excellence, continuous learning, and supports their professional goals.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on Data Engineering and Machine Learning 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
• Bachelor's degree or equivalent practical experience in a related field.
• Extensive hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, JAX, scikit-learn).
• Deep expertise in Cloud Engineering (AWS, Azure, GCP) and Distributed Micro-service architecture.
• Experienced with Kubernetes ecosystem, including EKS, Helm, and custom operators.
• Background in High Performance Computing, ML Hardware Acceleration (e.g., GPU, TPU, RDMA), or ML for Systems.
• Strategic thinker with the ability to craft and drive a technical vision for maximum business impact.
• Demonstrated leadership in working effectively with engineers, data scientists, and ML practitioners.

Preferred Qualifications, Capabilities, and Skills

• Strong coding skills and experience in developing large-scale AI/ML systems.
• Proven track record in contributing to and optimizing open-source ML frameworks.
• Recognized thought leader within the field of machine learning.
• Understanding & experience of AI/ML Platforms, LLMs, GenAI, and AI Agents.