AI ML Director

As an Applied AI/GenAI ML Director at JPMorgan Chase, you will play a pivotal role in collaborating with agile teams to enhance, create, and deliver cutting-edge technology products in a secure and scalable manner. Your expertise will be instrumental in challenging the norm, driving innovation, and leading the strategic development of new products and technology portfolios. By staying updated on industry trends and best practices, you will establish common capabilities and frameworks for AI excellence across the organization. - Establish and promote a library of common ML assets, including reusable models, features stores, data pipelines, and standardized templates. - Lead the creation of shared tools and platforms to streamline the end-to-end ML lifecycle. - Develop curative solutions using GenAI workflows and advanced proficiency in Large Language Models (LLMs). - Gain experience in creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines. - Provide guidance on the strategy and development of products, applications, and technologies. - Act as a lead advisor on the technical feasibility and business need for AIML use cases. - Collaborate with AI ML stakeholders across the organization. - Translate complex technical issues for leadership to drive innovation and informed decision-making. - Influence business, product, and technology teams while managing senior stakeholder relationships. - Champion a culture of diversity, opportunity, inclusion, and respect within the firm. Qualifications and Skills Required: - Formal training or certification in Machine Learning concepts with over 10 years of applied experience in programming languages like Python, Java, or C/C++. - MS and/or PhD in Computer Science, Machine Learning, or related field. - Strong understanding of ML techniques, especially in NLP and LLMs. - Experience in AI implementation in software development and legacy code transformation. - Familiarity with agentic workflows and frameworks like LangChain and LangGraph. - Proficiency in deep learning frameworks such as PyTorch or TensorFlow. - Knowledge in advanced ML areas like GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, and RAG. - Ability to work on system design from ideation to completion with limited supervision. - Excellent communication and teamwork skills, with a passion for detail and follow-through. - Demonstrated leadership in collaborating effectively with engineers, product managers, and ML practitioners. - Practical experience in cloud-native environments like AWS. Preferred Qualifications: - Experience with distributed training frameworks like Ray and MLFlow. - Understanding of advanced methodologies such as Embedding-based Search/Ranking, Recommender systems, and Graph techniques. - Proficiency in Reinforcement Learning or Meta Learning. - Deep knowledge of LLM techniques, including Agents, Planning, and Reasoning. - Experience in building and deploying ML models on AWS platforms using tools like Sagemaker and EKS.,