Director of Applied AI ML Engineering

Director - Applied AI ML (Software Engineering/Data & Agentic Systems)

Join our innovative team and shape the future of software development with AI-driven solutions.

As an Applied GenAI Engineering Director within the Ai4Tect Team at JPMorganChase, you will leverage deep software engineering and data engineering expertise to design, build, and deliver trusted, secure, stable, and scalable GenAI capabilities. You will partner with agile engineering teams to create production-grade agentic workflows and RAG-based systems, and to establish reusable frameworks (“golden paths”), shared components, and best practices that accelerate delivery and ensure consistency across business functions. You will stay current on GenAI engineering trends and translate complex technical tradeoffs into clear guidance for senior stakeholders, enabling informed decisions and measurable business impact.

Job Responsibilities

• Establish and promote a library of reusable GenAI/ML engineering assets, including reference implementations, standardized templates/SDKs, shared RAG components (ingestion, chunking, embedding, indexing, retrieval), and deployment patterns.
• Lead the creation of shared tools and platforms that streamline the end-to-end lifecycle for GenAI applications, including data pipelines, orchestration, evaluation, monitoring/telemetry, and release governance.
• Build and operationalize agentic GenAI workflows (planning/execution patterns, tool calling, state management, retries) with appropriate guardrails, permissions, and observability.
• Design and implement Generative AI evaluation and feedback loops (offline test suites, human review where needed, continuous evaluation, telemetry-based monitoring, regression gating in CI/CD).
• Advise on strategy and development across multiple GenAI products, applications, and technology portfolios—focusing on common capabilities that scale across teams rather than one-off solutions.
• Serve as a lead advisor on technical feasibility and business value for GenAI use cases, driving build-vs-buy decisions and pragmatic solution designs.
• Liaise with firmwide AI/ML stakeholders to drive standards, interoperability, adoption, and reuse of shared frameworks.
• Communicate complex technical issues and tradeoffs (quality vs latency vs cost; evaluation design; governance; security) to leadership to support well-informed strategic decisions.
• Influence across business, product, and technology teams; effectively manage senior stakeholder relationships; mentor engineers and practitioners to raise engineering and delivery standards.
• Champion the firm’s culture of diversity, opportunity, inclusion, and respect.

Key skills/competency

• Generative AI (GenAI)
• Machine Learning (ML) Engineering
• Software Engineering
• Data Engineering
• Agentic Workflows
• RAG Systems
• Python
• AWS
• Cloud-Native Architecture
• Distributed Systems