About the job
In this role, you will set technical direction, build and mentor high-performing teams, and establish rigorous practices for model development, evaluation, and governance. You’ll operate at the intersection of innovation and execution—accelerating adoption of AI capabilities (including LLM-driven workflows where appropriate) while ensuring reliability, safety, and measurable outcomes in a highly regulated environment.
Responsibilities
Establish and promote common AI assets to drive efficiency and scale across CIB use cases.
Design and deliver GenAI solutions using advanced large language models (LLMs) and related techniques.
Define robust evaluation frameworks and feedback loops for agentic systems and GenAI applications to ensure safety, accuracy, and continuous improvement.
Advise on strategy and the development of multiple products, applications, and technologies, aligning AI roadmaps with business outcomes.
Serve as the lead advisor on technical feasibility and business value for applied AI/ML use cases.
Liaise with firmwide AI/ML stakeholders to coordinate standards, governance, and adoption.
Translate complex technical issues, trends, and approaches for senior leadership to enable strategic, well-informed decisions on technology advancements.
Influence across business, product, and technology teams and successfully manage senior stakeholder relationships.
Ensure compliance with firm policies and applicable regulations, integrating model governance, risk controls, and monitoring into AI lifecycle practices.
Qualifications
Minimum
MS with 10+ years of experience or PhD with 5+ years of experience in Computer Science, Machine Learning, or a related field.
Formal training or certification in machine learning; with 5+ years of applied experience in one or more programming languages (e.g., Python, Java, C/C++).
Strong understanding of AI implementation in software development, including modernization of legacy codebases.
Deep expertise in LLM techniques (e.g., agents, planning, reasoning) and related methods.
Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph); verify any third-party tools are approved for use at JPMorgan Chase before implementation.
Experience with vector databases, scalable retrieval systems, and evaluation metrics for LLMs.
Hands-on experience in code and architecture, collaborating closely with engineering to productionize experimental results.
Strong proficiency with deep learning frameworks such as PyTorch or TensorFlow.
Solid understanding of ML techniques, especially in Natural Language Processing (NLP) and LLMs.
Experience in leading technologies to anticipate, manage, and resolve complex technical challenges within your domain.
Preferred
Understanding of Embedding based Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
using Advanced knowledge in Reinforcement Learning or Meta Learning.
Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.
Background in model governance, bias mitigation, and responsible AI practices with track record of delivering AI platforms or shared services used across multiple lines of business.