at J.P. Morgan
Bulge Bracket Investment BanksPosted 10 days ago
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**Executive Product Solutions Director (Data, AI & GenAI Architecture)** leads cross-functional teams to shape customer-centric, scalable AI/ML solutions for our Firmwide Data and AI Platform. Key responsibilities involve advising product teams, partnering with TAMs, designing cloud-native architectures, and ensuring security compliance. Required qualifications include 8+ years of experience in multiple-product solutioning, cloud platforms (AWS/Azure/GCP), data engineering, and DevOps. Lead solution architecture reviews, mentor teams, and bridge tech with enterprise needs to drive customer experience enhancements.
- Compensation
- Not specified
- City
- New York City
- Country
- United States
Currency: Not specified
Full Job Description
Location: New York, NY, United States
Unlock your potential as a leader in product solutions and Generative AI architecture. In this role, you will lead and enable Technical Account Managers (TAMs), shape customer-centric platform adoption strategies, and define scalable GenAI/ML system architectures across the Firmwide Data and AI Platform product cluster. You will act as the technical bridge between business strategy, data science, and cloud software engineering balancing innovation with enterprise requirements such as cost, latency, data privacy, and compliance.
As a Executive Product Solutions Director in the Firmwide Data and AI Platform team, and as a senior leader, you will craft complex, end-to-end solutions, assess and govern risk, capture market opportunities, and establish repeatable processes that allow teams to continuously deliver value to customers in a fast-paced, collaborative environment.
Job responsibilities
- Advise Product Solutions teams on solutioning and adoption of new and existing client-facing products and capabilities, crafting complex solutions and assessing risk to enhance customer experience.
- Partner with TAMs (Technical Account Managers) to identify, shape, and capture market opportunities; develop solution strategies that scale across customers and use cases.
- Lead the strategic development of end-to-end product solution strategies and processes across a cluster of products (Data and AI Platforms), ensuring consistency, quality, and delivery velocity.
- Manage the collection of client feedback and ensure structured, actionable delivery of feedback to Product teams to influence roadmap and prioritization.
- Design scalable, cloud-native architectures for Generative AI systems, including Retrieval-Augmented Generation (RAG), agentic workflows, and traditional machine learning solutions.
- Architect data platforms and pipelines (data lakes/lakehouses, ETL/ELT, embedding generation, vector database integrations) to support secure, reliable AI applications.
- Evaluate, select, and integrate foundation models (for example; GPT-family, Claude, open-source LLMs), enabling model orchestration via frameworks (for example; LangChain, LlamaIndex) or custom microservices.
- Establish MLOps/LLMOps governance: CI/CD patterns, model monitoring, drift detection, evaluation (including hallucination testing), and AI safety guardrails suitable for production.
- Ensure solutions adhere to enterprise security and compliance requirements, including data privacy, RBAC, and intellectual property protection.
- Lead architecture design reviews and decision documentation (for example; ADRs), mentor engineering teams, and translate technical trade-offs into business value.
Required qualifications, capabilities, and skills
- 8+ years of experience or equivalent expertise leading and developing solutions across multiple teams and a cluster of products
- Demonstrable experience structuring and handling complex solutions for business problems to meet clients needs
- Extensive experience facilitating internal platform adoption activities and developing and optimizing strategies and processes
- Strong ability to operate as a senior technical and strategic advisor communicating trade-offs clearly to technical and non-technical stakeholders.
- Cloud platforms: AWS, Azure, and/or GCP.
- Data engineering: SQL, Python, Apache Spark; familiarity with data warehouses/lakehouse ecosystems (for example; Snowflake, Databricks); event streaming (for example; Kafka).
- DevOps/MLOps: Docker, Kubernetes, CI/CD tooling, model/version management, and evaluation benchmarks (including LLM evaluation approaches).
Preferred qualifications, capabilities, and skills
- Hand on experience developing solutions specifically for Data and AI Platforms
- Deep practical experience with GenAI patterns and tooling: RAG, vector databases (for example; Pinecone, Milvus, pgvector), fine-tuning, and multi-agent systems.
- Self-starter, comfortable operating with the ambiguity and helping stand up a new team/function (defining operating model, playbooks, governance, and repeatable delivery mechanisms).
