Executive Product Solutions Director (Data, AI & GenAI Architecture)

JPMorgan Chase & Co.; City of Rochester (NY); Sur place; USD 250 000 - 350 000; Plein temps

Résumé du poste

JPMorgan Chase & Co. in New York is seeking an Executive Product Solutions Director to lead GenAI architecture across the Firmwide Data and AI Platform. You will drive TAM leadership, craft customer-centric adoption strategies, and shape scalable AI/ML architectures that balance cost, latency, privacy, and compliance.

You will govern risk, capture market opportunities, and establish repeatable processes that enable teams to deliver value quickly in a fast-paced, collaborative environment.

Qualifications

• 8+ years of experience leading and developing solutions across multiple teams and products.
• Ability to structure complex solutions for client needs and manage risk.
• Experience enabling platform adoption and defining scalable processes.

Responsabilités

• Advise Product Solutions teams on solutioning and adoption of client-facing products and capabilities, crafting complex solutions and assessing risk to enhance customer experience.
• Partner with TAMs 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 across 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 ML solutions.
• Architect data platforms and pipelines (data lakes/lakehouses, ETL/ELT, embedding generation, vector databases) to support secure AI applications.
• Evaluate, select, and integrate foundation models (GPT-family, Claude, open-source LLMs), enabling model orchestration via LangChain or custom microservices.
• Establish MLOps/LLMOps governance: CI/CD patterns, model monitoring, drift detection, evaluation, and AI safety guardrails for production.
• Ensure solutions adhere to security and compliance requirements including data privacy, RBAC, and IP protection.
• Lead architecture design reviews and document decisions (ADRs), mentor engineers, and translate tech trade-offs into business value.

Connaissances

Solution leadership

Executive advisory

GenAI architecture

Cloud platforms

Data engineering

DevOps/MLOps

Communication

Stakeholder management

Outils

Docker

Kubernetes

CI/CD tooling

Model/version management

Description du poste

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).