Applied AI ML Director
On-site · Bengaluru, Karnataka, India
Job Summary
Applied AI/GenAI ML Director within JPMorgan Chase’s Asset and Wealth Management Technology Team. Defines architectures for agentic AI including LLM orchestration, tool use, retrieval, and safety; leads hands-on Python work with PyTorch or TensorFlow; builds reusable components (prompt management, evaluators, safety filters, memory stores, connectors, RAG pipelines); implements CI/CD, feature stores, experiment tracking, automated model testing, drift monitoring, versioning, lineage, and rollback with SLOs for latency, accuracy, resiliency, and cost per inference. Builds data pipelines for structured and unstructured data, document ingestion, embeddings, and retrieval; enforces data quality and access controls. Integrates content safety, audit logging, RBAC, and aligns with model risk governance and privacy. Optimizes inference performance, manages cloud cost profiles (AWS/Azure), and drives engineering standards, code quality, and incident response. Delivers APIs and microservices integrating LLM/agent capabilities into client/advisor workflows and analytics platforms. Sets product vision, multi-quarter roadmaps, and priorities for agentic AI products aligned to business outcomes; manages backlog, governance, stakeholder engagement, and adoption. Ensures change readiness for advisors and operations; embeds responsible AI practices, explainability, monitoring, and risk controls with model risk, privacy, cyber, and third-party risk teams. Manages investment cases, budgets, ROI tracking, and vendor engagements; defines documentation standards and knowledge base for ongoing maintenance.
Required Qualifications
- Formal training or certification on Machine Learning concepts
- 10+ years applied experience
- Hands-on experience building agentic AI solutions and LLM orchestration
- 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
- MLOps expertise: CI/CD, model governance, monitoring, incident response
- Product leadership capabilities: roadmap ownership, backlog management, stakeholder engagement, adoption/change management
- Clear, concise communication with senior technical and business stakeholders
- Familiarity with version control, secure SDLC practices, and enterprise controls
- 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
- Knowledge of Asset & Wealth Management workflows and financial products; understanding of risk and compliance considerations for AI in finance
- Experience with model fine-tuning, adapters, and evaluation frameworks
- Hands-on ownership of defining and executing AI product strategy and governance
Desired Qualifications
- Formal training or certification in Machine Learning
- 10+ years applied experience in AI/ML
- Hands-on experience building agentic AI solutions and LLM orchestration (prompt engineering, tool use, retrieval, evaluators, guardrails)
- Strong Python engineering skills 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, stakeholder engagement, adoption/change management
- Clear, concise communication with senior technical and business stakeholders
- Familiarity with version control, secure SDLC practices, and enterprise controls
- 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
- Knowledge of Asset & Wealth Management workflows and financial products; understanding of risk and compliance considerations for AI in finance
- Experience with model fine-tuning, adapters, and evaluation frameworks
- Hands-on ownership of defining and executing AI product strategy and governance
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