As a Senior Lead Architect at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you will play a pivotal role in designing and governing enterprise-scale architecture solutions for software applications and platform products You will drive significant business impact by architecting next-generation AI/ML systems, influencing technology direction, and ensuring our solutions meet the highest standards of security, resiliency, and regulatory compliance.

Job responsibilities

• Represent product families in technical governance bodies, proposing enhancements to architecture governance and AI risk management practices.
• Provide strategic technical guidance to business stakeholders, engineering teams, contractors, and vendors, fostering a collaborative and innovative environment.
• Leverage enterprise-authorized AI/ML capabilities—including LLMs, agentic systems, and embedding pipelines—to accelerate architecture analysis, decisioning, and solution delivery, with robust human-in-the-loop validation and sensitive data handling.
• Guide evaluation and integration of current and emerging technologies, influencing peers and decision-makers to adopt leading-edge AI/ML and cloud-native solutions.
• Drive architectural decisions impacting product design, application functionality, and technical operations, with a focus on AI-enabled engineering patterns and governance.
• Develop secure, high-quality production code for data-intensive and AI-driven applications; review and debug code written by others to ensure best practices.
• Serve as a subject matter expert in data engineering, platform architecture, and AI/ML, actively contributing to the engineering community and advocating firmwide SDLC frameworks.
• Establish and govern reuse-first, AI-enabled engineering patterns across SDLC/toolchain practices, ensuring traceability, auditability, resiliency, and security controls.
• Architect and govern agentic AI systems—including multi-agent workflows, tool-use patterns, and human-in-the-loop controls—suitable for regulated financial services environments.
• Lead AI risk governance design, observability, and ability to explain requirements for production AI systems, shaping enterprise approaches to AI agent orchestration, inter-agent communication, and state management at scale.

Required qualifications, capabilities, and skills

• Formal training or certification on architecture concepts and 5+ years applied experience in AI/ML, cloud, and data engineering
• Minimum 12+ years of hands-on experience in system design, application development, testing, and operational stability.
• Demonstrated expertise in designing and deploying production AI/ML systems, including LLM-based applications, embedding pipelines, vector stores, and agentic architectures with tool use, memory, and multi-step reasoning.
• Experience evaluating model outputs for safety, accuracy, and latency in regulated environments.
• Advanced proficiency in programming languages such as Java and Python.
• Deep knowledge of software architecture, applications, and technical processes within disciplines such as cloud, artificial intelligence, machine learning, and data engineering.
• Working knowledge of relational and NoSQL databases, data lake architectures, and large-scale data processing technologies (e.g., Spark/PySpark, Databricks, Snowflake).
• Experience with microservices, API design, Kafka, Redis, Memcached, observability tools (Dynatrace, Splunk, Grafana), and orchestration tools (Airflow, Temporal).
• Ability to evaluate and integrate AI-enabled capabilities into enterprise-grade architectures, meeting resiliency, security, and auditability requirements.
• Practical cloud-native experience and ability to tackle complex design and functionality challenges independently.
• Strong judgment and communication skills to influence technical direction across teams and stakeholders.

Preferred qualifications, capabilities, and skills

• Experience with modern data technologies such as Databricks or Snowflake.
• Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, CrewAI, or equivalent) and model serving infrastructure (Triton, AWS Bedrock, Azure Open AI).
• Familiarity with AI evaluation and observability—red-teaming, evals frameworks, prompt drift detection, and cost/latency monitoring for LLM workloads.
• Understanding of agentic design patterns: React, plan-and-execute, reflection loops, and how to constrain agent autonomy in high-stakes financial workflows.
• Awareness of the AI regulatory landscape in financial services, especially regarding AI use in decision-making.
• Knowledge of the financial services industry and their IT systems.