Director - Applied AI ML (Software Engineering/Data & Agentic Systems)
JPMorganChase · Bengaluru, Karnataka, India
- On site
- Full-time
- $250,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Lead GenAI engineering and agentic systems development.
- Build scalable, secure, and production-grade AI capabilities.
- Establish reusable frameworks and best practices.
- Advise senior stakeholders on AI strategy and impact.
- Drive innovation in financial technology solutions.
About the role
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
Skills & topics
- Director
- Applied AI
- Machine Learning
- ML Engineering
- Software Engineering
- Data Engineering
- Agentic Systems
- GenAI
- RAG
- Python
- AWS
- Distributed Systems
- Cloud-Native
- Financial Services
- AI Strategy
- Leadership
How to get hired
- Tailor your resume: Highlight your 10+ years of software/data engineering experience, GenAI production deployment, RAG, and agentic workflow expertise.
- Showcase cloud skills: Emphasize your AWS cloud-native experience, secure deployment, and cost/latency management.
- Demonstrate leadership: Provide examples of translating complex technical issues for senior stakeholders and influencing cross-functional teams.
- Prepare for technical deep dives: Be ready to discuss distributed systems, data architecture, and specific GenAI frameworks you've used.
- Understand company values: Align your communication with JPMorganChase's emphasis on diversity, inclusion, and respect.
Technical preparation
Master Python, Java, or similar for distributed systems.,Build and deploy RAG and agentic GenAI systems.,Implement secure cloud-native solutions on AWS.,Design robust data architecture and pipelines.
Behavioral questions
Describe a complex technical issue you resolved.,How do you influence senior stakeholders?,Share an example of mentoring engineers.,How do you promote diversity and inclusion?
Frequently asked questions
- What is the primary focus of the Director - Applied AI ML role at JPMorganChase?
- The Director - Applied AI ML role at JPMorganChase focuses on leading the design, build, and delivery of trusted, secure, and scalable Generative AI (GenAI) capabilities. This includes developing agentic workflows and RAG-based systems, establishing reusable frameworks, and advising senior stakeholders on AI strategy and technical feasibility within the financial services industry.
- What are the key technical skills required for the Applied AI ML Director position?
- Key technical skills include 10+ years of software/data engineering experience with Python, Java, or similar; hands-on experience with GenAI systems, RAG (embeddings, retrieval/indexing), and agentic workflows (e.g., LangChain/LangGraph); strong data architecture understanding; and cloud-native experience on AWS, including secure deployment and operations.
- What kind of impact can I expect to make in this Director role at JPMorganChase?
- In this role, you will significantly impact the organization by establishing reusable GenAI/ML engineering assets, leading the creation of shared tools and platforms, and operationalizing agentic GenAI workflows. Your work will streamline the GenAI application lifecycle, improve efficiency, and drive measurable business impact through informed strategic decisions.
- How does JPMorganChase approach diversity and inclusion for this Applied AI ML Director role?
- JPMorganChase is an equal opportunity employer that highly values diversity and inclusion. They champion a culture of diversity, opportunity, inclusion, and respect, and make reasonable accommodations for applicants and employees' needs. This commitment extends to all aspects of employment.
- What is the importance of RAG and agentic workflows in this Director of Applied AI ML position?
- RAG (Retrieval-Augmented Generation) and agentic workflows are central to this role. You will be responsible for building production-grade RAG-based systems and operationalizing agentic GenAI workflows, including planning/execution patterns, tool calling, and state management, to create advanced AI capabilities.
- How does this role contribute to the broader AI/ML strategy at JPMorganChase?
- This role contributes by advising on strategy and development across multiple GenAI products, focusing on scalable common capabilities. You will liaise with firmwide AI/ML stakeholders to drive standards, interoperability, adoption, and reuse of shared frameworks, ensuring a cohesive and efficient AI ecosystem.