Help shape how GenAI and agentic AI create value at JPMorgan Chase. In this hands-on leadership role, you will lead and grow a team of data scientists while setting technical direction and delivery standards. You will partner closely with engineering, product, and business teams to move from ideas to production-grade solutions. If you enjoy building, mentoring, and influencing outcomes across teams, you will find meaningful opportunities to grow your impact here.

Job summary

As an Applied AI and Machine Learning Director in our Applied AI and Machine Learning team, you will lead end-to-end delivery of GenAI and AI and machine learning solutions and guide technical decisions that enable scalable, maintainable outcomes. You will mentor and develop a team, establish best practices for code quality and model delivery, and stay close to the work through architecture and critical reviews. You will collaborate across functions to translate needs into clear plans, success metrics, and shipped capabilities. You will communicate progress and risks to senior leaders and connect technical outcomes to business impact.

You will work across multiple concurrent initiatives and balance near-term delivery with longer-term technical foundations. You will help drive adoption of new capabilities by supporting change and enabling partners to use solutions effectively. You will contribute to a culture of innovation, continuous improvement, and inclusive team practices.

Job responsibilities

• Lead, mentor, and develop a team of data scientists, fostering a collaborative, high-performance, and inclusive environment
• Set clear goals, provide technical guidance, and support professional growth for team members
• Establish and reinforce best practices in data science, code quality, and solution delivery
• Oversee end-to-end delivery of GenAI and AI and machine learning use cases from ideation and prototyping through production deployment
• Contribute hands-on to solution architecture, technical design, and critical code reviews
• Design and implement GenAI solutions using large language models, agentic AI systems, and retrieval augmented generation patterns
• Build and optimize scalable data pipelines and ETL and ELT processes to support model development and deployment
• Partner with engineering teams to deliver robust, scalable, and maintainable solutions
• Collaborate with product and business partners to clarify requirements, align priorities, and manage expectations
• Develop project plans, define success metrics, and drive execution across multiple initiatives while identifying and mitigating risks
• Communicate clearly with senior leaders on progress, risks, and the business impact of technical solutions

Required qualifications, capabilities, and skills

• Advanced Python programming skills, including writing production-quality, maintainable code
• Hands-on experience delivering GenAI solutions using large language models, including prompt engineering and fine-tuning
• Experience with agentic AI frameworks such as Lang Chain, Llama Index, Auto Gen, or CrewAI
• Experience with retrieval augmented generation patterns, embeddings, vector search, and vector databases
• Experience with modern data platforms and ETL and ELT practices, including Snowflake or Databricks and at least one major cloud platform
• Experience with common data science and machine learning libraries such as pandas, Num Py, scikit-learn, Py Torch, or Tensor Flow
• Working knowledge of MLOps practices such as model versioning, experiment tracking, deployment pipelines, Git, and containerization
• Proven people leadership experience leading and developing data science teams with a focus on technical excellence and growth
• Demonstrated delivery of business-impactful AI and machine learning initiatives in large enterprise environments
• Strong project management and stakeholder management skills across cross-functional teams, including communication to senior leaders
• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or equivalent practical experience

Preferred qualifications, capabilities, and skills

• Experience with graph databases and semantic technologies such as Neo4j, Tiger Graph, or Amazon Neptune
• Experience with knowledge graph construction, ontology design, taxonomy development, and integration with GenAI solutions
• Experience with advanced retrieval techniques such as hybrid search, re-ranking, query decomposition, or multi-hop reasoning
• Experience with model evaluation approaches and responsible AI practices
• Experience enabling adoption of AI and machine learning solutions through change management or organizational enablement
• Familiarity with Agile delivery methods and modern product practices
• Experience in financial services or other highly regulated industries