Senior Lead Architect: Solution Architecture

JPMorgan Chase & Co.•Jersey City, NJ

2d•$171,000 - $260,000

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.