Applied AI ML Director

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together

As an Applied AI/GenAI ML Director within the Asset and Wealth Management Technology Team at JPMorgan Chase, you will provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will leverage your deep expertise to consistently challenge the status quo, innovate for business impact, lead the strategic development behind new and existing products and technology portfolios, and remain at the forefront of industry trends, best practices, and technological advances. This role will focus on establishing and nurturing common capabilities, best practices, and reusable frameworks, creating a foundation for AI excellence that accelerates innovation and consistency across business functions

Job Responsibilities:

• Defines and owns reference architectures for agentic AI, including LLM orchestration, tool use, retrieval, guardrails, evaluation harnesses, and observability. Lead hands-on build in Python with PyTorch or TensorFlow where needed.
• Establishes reusable components (prompt management, evaluators, safety filters, memory stores, connectors, RAG pipelines) to accelerate delivery across AWM and partner lines of business.
• Implements CI/CD, feature stores, experiment tracking, automated model testing, drift monitoring, versioning, lineage, and rollback. Ensure SLOs for latency, accuracy, resiliency, and cost per inference.
• Builds pipelines for structured and unstructured data, document ingestion, embeddings, and retrieval. Enforce data quality, metadata standards, and access controls.
• Integrates content safety, policy enforcement, audit logging, and role-based access. Align with model risk governance, privacy requirements, and responsible AI guidelines.
• Optimizes inference performance, caching, batching, prompt templates, and model selection. Manage cloud cost profiles and capacity planning (AWS or Azure).
• Drives engineering standards, code quality, design reviews, threat modeling, and incident response. Coach teams; remove blockers; enforce accountability on deliverables. Deliver APIs and microservices integrating LLM/agent capabilities into client and advisor workflows, internal ops, and analytics platforms.
• Defines product vision, multi-quarter roadmap, and portfolio priorities for agentic AI products aligned to AWM business outcomes (revenue, efficiency, risk reduction, client experience). Translates use cases into measurable outcomes with clear success metrics (e.g., hours automated, cycle-time reduction, error-rate reduction, risk control adherence, client NPS impact).
• Owns product backlog, value sizing, and sequencing across competing demands. Make firm trade-offs; set decision rights; escalate strategically. Engages senior business leaders, legal/compliance, risk, and operations. Communicate plans, progress, and KPIs clearly; secure approvals; manage expectations.
• Drives rollout plans, training, enablement assets, and support model. Ensure change readiness for advisors, operations, and technology stakeholders; track adoption funnel and usage. Embed responsible AI practices, explainability, and monitoring; partner with model risk, privacy, cyber, and third-party risk to ensure compliant deployment and sustained operations.
• Owns investment cases, budgets, and ROI tracking. Make build/buy/partner decisions; manage vendor engagements subject to firm approvals. Defines support processes, SLAs, and service management with Tech Ops/SRE. Establish documentation standards and knowledge base for ongoing product maintenance.

• Formal training or certification on 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.
• Hands-on experience building agentic AI solutions and LLM orchestration (prompt engineering, tool use, retrieval, evaluators, guardrails).
• Strong Python engineering skills; experience with PyTorch or TensorFlow.
• Proven delivery of APIs/microservices integrating LLM/NLP with business applications.
• Data engineering experience for structured and unstructured data; embeddings and retrieval pipelines.
• Cloud deployment experience (AWS or Azure) for AI/ML workloads; reliability, scalability, and cost optimization.
• MLOps expertise: CI/CD, model governance, monitoring, incident response.
• Product leadership capabilities: roadmap ownership, backlog management, business case development, stakeholder engagement, and adoption/change management.
• Clear, concise communication with senior technical and business stakeholders; can present trade-offs and decisions.
• Familiarity with version control, secure SDLC practices, and enterprise controls.

• Experience with model fine-tuning, adapters, and evaluation frameworks.
• Knowledge of Asset & Wealth Management workflows and financial products; understanding of risk and compliance considerations for AI in finance.
• Experience defining and tracking product OKRs/KPIs (e.g., automation hours, cost per inference, adoption rates, control adherence).
• Experience managing vendor solutions and third-party risk within an enterprise environment.