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$150,000–$222,500.
Based on 240 similar postings.
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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As a Senior Lead Software Engineer - Agentic AI SRE Platform within the AI/ML Data Platforms organization, you will join the Reliability Engineering team to lead the architecture and development of production-grade agentic AI and multi-agent systems. You will design autonomous agents that reason, analyze, and act across telemetry pipelines, application stacks, and cloud infrastructure to improve engineering productivity and system reliability. Your daily work involves architecting orchestration, memory management, and workflow control while managing scalable AI infrastructure and deployment pipelines. You must utilize Python, AWS, and Terraform alongside frameworks like LangGraph and CrewAI. The role focuses on solving complex reliability problems by building systems that automate remediation of recurring issues, manage retrieval-augmented generation, and implement vector search within high-scale distributed systems.
Design and develop enterprise AI systems to enhance engineering productivity and system reliability.
Architect autonomous agents that reason, analyze, and act across system and application reliability.
Govern agentic components including orchestration, memory management, workflow control, and human-in-the-loop patterns.
Own the non-functional requirements posture for agentic systems, including observability, security, and guardrails.
Provision and manage scalable AI infrastructure, deployment pipelines, and cost optimization.
Develop secure production code and perform code reviews and debugging for other team members.
Identify and automate the remediation of recurring issues to improve operational stability.
Lead evaluation sessions with external vendors and internal teams to probe architectural designs and technical credentials.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of applied experience.
Advanced proficiency in programming with Python.
Proficiency in all aspects of the Software Development Life Cycle, automation, and continuous delivery methods.
Hands-on experience delivering agentic AI to production.
Experience with AI agent frameworks, runtimes, orchestration, evaluation/benchmarking systems, and model selection policies.
Experience developing production generative AI applications at scale with experimentation, model quality measurement, RAG, and vector search.
Strong cloud infrastructure experience (e.g., AWS) and infrastructure-as-code (e.g., Terraform).
Experience building distributed systems or high-scale systems where reliability matters.
Previous experience working within infrastructure, platform engineering, or reliability engineering domains (preferred).
Knowledge of site reliability engineering concepts, including SLOs/SLIs, error budgets, incident management, resilience patterns, and observability (preferred).
Previous experience as a machine learning software engineer in a dynamic technology company or startup (preferred).