<p>JobID: 210715303</p> <p>Category: Predictive Science</p> <p>JobSchedule: Full time</p> <p>Posted Date: 2026-03-04T22:00:00+00:00</p> <p>JobShift: Day</p> <p>Base Pay/Salary: Jersey City,NJ $164,350.00-$260,000.00; New York,NY $164,350.00-$260,000.00; Chicago,IL $137,750.00-$235,000.00</p> <p>Position Overview</p> <p>Join our Applied AI/ML team as an AI/ML Solutions Lead (Vice President) driving high-impact GenAI initiatives across Consumer & Community Banking. This is a hands-on GenAI-focused data science position requiring a proven track record of delivering business-impactful projects from conception through scaled deployment. You will own the end-to-end lifecycle of GenAI use cases, serving as both technical leader and strategic partner while maintaining hands-on involvement in solution architecture, prototyping, and implementation.</p> <p>Core Responsibilities</p> <p>Strategic Delivery & Technical Leadership</p> <ul> <li>Own end-to-end delivery of GenAI and AI/ML use cases with ability to independently plan, anticipate complexities, and deliver high-quality outputs within agreed timelines </li><li>Hands-on development and prototyping of GenAI solutions, including building POCs, architecting scalable solutions, and writing production-quality code </li><li>Design and implement GenAI solutions leveraging LLMs, agentic AI systems, and RAG architectures </li><li>Build end-to-end data pipelines using Python and modern platforms (Snowflake, Databricks), implementing ETL/ELT processes for model development </li><li>Proactively identify and communicate risks, delays, and mitigation options with structured progress updates </li></ul> <p>Thought Leadership & Stakeholder Management</p> <ul> <li>Develop and evangelize strategic vision for GenAI solutions, generating clarity in uncertain environments through deep customer engagement </li><li>Proactively engage with stakeholders to confirm expectations, surface uncertainties early, and maintain alignment throughout project lifecycles </li><li>Own communication cadence with executive stakeholders, providing forward-looking updates without repeated prompting </li><li>Demonstrate analytical rigor and storytelling, clearly linking findings, implications, and recommended actions </li></ul> <p>Execution & Change Management</p> <ul> <li>Independently develop comprehensive project plans, identify stakeholders, define success metrics, and drive execution with minimal direction </li><li>Develop and implement best practices for integrating GenAI solutions as scalable, enterprise-grade capabilities </li><li>Collaborate across cross-functional teams to identify strategic partners and foster collaborative environments </li></ul> <p>Required Qualifications</p> <p>Critical Technical Skills</p> <ul> <li> <p>Advanced Python programming with ability to write production-quality, maintainable code for data science and AI/ML applications</p> </li><li> <p>Hands-on GenAI experience:</p> </li><li> <p>Large Language Models (e.g. GPT, Claude, Llama) including prompt engineering and fine-tuning</p> </li><li> <p>Agentic AI frameworks (e.g. LangChain, LlamaIndex, AutoGen, CrewAI)</p> </li><li> <p>RAG architectures including vector databases (Pinecone, Weaviate, ChromaDB, FAISS), embedding models, and retrieval optimization</p> </li><li> <p>Modern data platforms:</p> </li><li> <p>Snowflake for data warehousing and analytics</p> </li><li> <p>Databricks for distributed computing and ML workflows</p> </li><li> <p>ETL/ELT processes and data pipeline orchestration</p> </li><li> <p>Cloud platforms (AWS, Azure, GCP) and their AI/ML services</p> </li><li> <p>Core data science packages: pandas, numpy, scikit-learn, PyTorch/TensorFlow</p> </li><li> <p>MLOps practices: model versioning, experiment tracking (MLflow), deployment pipelines, version control (Git), containerization (Docker)</p> </li></ul> <p>Critical Business & Leadership Capabilities</p> <ul> <li>Proven track record of delivering business-impactful GenAI/AI/ML projects in large enterprise environments from ambiguous requirements to scaled production </li><li>End-to-end project planning and execution with ability to independently manage timelines, resources, risks, and stakeholder expectations across concurrent initiatives </li><li>Operating in uncertainty: demonstrated capability to generate clarity through structured problem-solving, customer engagement, and iterative refinement </li><li>Excellent communication and stakeholder management with proven ability to influence senior leaders and drive alignment across diverse teams </li><li>Self-directed work style with ability to anticipate complexities, proactively identify risks, and maintain disciplined progress with limited supervision </li><li>Strong analytical and problem-solving skills with demonstrated rigor in structuring problems and developing actionable recommendations </li></ul> <p>Education</p> <ul> <li>Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or equivalent practical experience </li></ul> <p>Preferred Qualifications</p> <p>Advanced Technical Skills</p> <ul> <li> <p>Advanced degree (Master's or PhD) in Computer Science, Data Science, Machine Learning, or related quantitative field</p> </li><li> <p>Semantic technologies and graph databases:</p> </li><li> <p>Property graphs and graph databases (Neo4j, TigerGraph, Amazon Neptune) with Cypher query language</p> </li><li> <p>RDF graphs and semantic web technologies (RDF, OWL, SPARQL)</p> </li><li> <p>Knowledge graph construction, ontology design, and taxonomy development</p> </li><li> <p>Integration of graph-based approaches with GenAI solutions (GraphRAG, knowledge-enhanced LLMs)</p> </li><li> <p>Advanced RAG techniques: hybrid search, re-ranking, query decomposition, multi-hop reasoning</p> </li><li> <p>Experience with model evaluation frameworks and responsible AI practices</p> </li></ul> <p>Business & Leadership</p> <ul> <li>Experience with change management principles and organizational adoption of AI/ML capabilities </li><li>Familiarity with Agile methodologies and modern product management frameworks </li><li>Track record of thought leadership through publications, presentations, or recognized contributions </li><li>Experience in financial services or highly regulated industries </li><li>Demonstrated ability to mentor team members and set high standards for execution quality </li></ul> <p>What Sets Successful Candidates Apart</p> <ul> <li>Technical Excellence: Portfolio of GenAI projects showcasing hands-on expertise in RAG systems, agentic AI, or LLM-powered applications with measurable business impact </li><li>Delivery Excellence: Consistent track record of delivering complex projects on time with high quality, navigating ambiguity through structured approaches </li><li>Business Impact Orientation: Clear examples of GenAI/AI/ML solutions that drove measurable business value with articulated linkage between technical solutions and outcomes </li><li>Proactive Leadership: Evidence of independently driving initiatives forward, anticipating obstacles, and maintaining momentum without constant direction </li><li>Semantic & Graph Expertise (Plus): Experience applying knowledge graphs, semantic layers, or graph-based reasoning to enhance GenAI solutions </li></ul> <p>Technical Environment</p> <p>Languages: Python, SQL | GenAI: SmartSDK, LangChain, LlamaIndex, OpenAI/Anthropic APIs | Data Platforms: Snowflake, Databricks, AWS/Azure/GCP | ML/DL: PyTorch, TensorFlow, scikit-learn | Vector DBs: Pinecone, Weaviate, ChromaDB, FAISS | Graph DBs: Neo4j, TigerGraph, Amazon Neptune | MLOps: MLflow, Docker, Kubernetes, Git</p> <p>JPMorgan Chase is an equal opportunity employer committed to creating an inclusive environment for all employees.</p>