Lead Software Engineer - Python - GenAI

JPMorgan Chase & Co.; Plano (TX); Hybrid; USD 150,000 - 190,000; Full time

Job summary

JPMorgan Chase & Co. seeks an experienced engineer to lead design and delivery of scalable ML-powered services. You will architect resilient microservices, integrate with enterprise systems, and drive AI-assisted practices across teams.

You will mentor developers, collaborate with architects and DBAs, productionalize ML models, and ensure secure, auditable delivery in a fast-paced environment.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience; hands-on design and operations.
• Deep distributed-systems engineering with Python/Java; modern microservices in production.
• Strong Python experience for AI/ML engineering including tooling, monitoring, and governance.
• Experience leading AI-assisted software development tools and setting validation expectations.
• Knowledge of secure coding, resiliency, and enterprise-grade SDLC practices.
• Proficiency in Linux environments and Kubernetes orchestration for production services.
• Experience with Kafka or similar messaging, and observability tools like ELK or Splunk.
• API design with REST and service-oriented architectures; strong communication and cross-stakeholder influence.
• AI/ML platform exposure including MLOps, feature engineering, and model hosting.

Responsibilities

• Architect and deliver scalable, low-latency services; drive target-state architecture.
• Design and deploy services with enterprise integration, ensuring security, governance, and auditability.
• Lead and mentor development teams; manage multiple deliverables across groups.
• Collaborate with LOB users, SMEs, architects, DBAs, and admins to design solutions and resolve issues.
• Mature ML pipelines for fraud detection and risk assessment; support modeling teams.
• Productionalize models with validation readiness and quality controls for live use.
• Promote AI-assisted engineering practices and standardized validation across the team.
• Utilize SDLC tools, automations, and governance to improve automation value.
• Own reusable ML platform components and establish monitoring for performance and reliability.
• Build agentic AI services to automate engineering/workflow tasks and ensure traceability.
• Define guardrails and evaluation approaches for agentic AI in production.

Skills

Python

Java

Kubernetes

Linux

AI/ML

REST APIs

Distributed systems

CI/CD

Security

Mach Learn Ops

Tools

Kafka

ELK

Splunk

NoSQL Cassandra

Job description

Job responsibilities

• This role spans architecture, hands-on delivery, and ML/AI enablement in production. Architect and implement resilient, highly scalable, fault-tolerant, low-latency services and drive target-state architecture.




• Design and deploy services that integrate with enterprise systems; ensure functional, performance, scalability, security, governance, and auditability requirements are met.




• Lead and mentor the development team in a high-pressured delivery environment; manage multiple deliverables across business groups and strengthen stakeholder relationships.




• Collaborate with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues.




• Build and mature capabilities that execute ML pipelines for fraud detection and risk assessment; support modeling teams in implementation and tooling.




• Productionalize models built by data scientists, including validation readiness and quality controls prior to live usage.




• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.




• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.




• Design and own reusable ML platform components (e.g., feature-store patterns, delivery pipelines) and establish monitoring/alerting for performance, scalability, availability, and reliability.




• Build agentic AI services to automate and enhance engineering and model-ops workflows (tool-using agents, orchestration, state management, and audit-ready traceability).




• Define and implement guardrails and evaluation approaches for agentic AI in production (quality, safety, latency, and cost).

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience; Hands-on practical experience delivering system design, application development, testing, and operational stability




• The successful candidate demonstrates deep distributed-systems engineering expertise in Python/Java plus strong platform, delivery, and production-operability discipline; Recent hands-on software development experience in large-scale distributed systems, primarily Python and modern microservices.




• Strong Python experience for AI/ML engineering, automation, model operationalization, and agentic AI services, including tool integration, monitoring, telemetry, and governance; Strong experience with REST APIs and service-oriented / microservices architecture.




• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.




• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.




• Experience developing in Linux environments.




• Strong Kubernetes orchestration experience (building, deploying, and operating production services).




• Messaging expertise with Kafka, MQ, or similar platforms.




• Experience with backend infrastructure patterns (e.g., load balancing, autoscaling; Experience with NoSQL databases such as Cassandra; Experience with log analytics / observability tools (e.g., ELK, Splunk).




• Strong SDLC knowledge and agile ways of working, including CI/CD, application resiliency, security, testing, and operational stability; Strong communication skills and proven ability to influence across senior technology and business stakeholders.




• AI/ML platform exposure (MLOps, feature engineering, model hosting/operationalization; AWS and/or hybrid on-prem + cloud); Agentic AI experience: building and operating LLM-driven agents with tool integration, monitoring/telemetry, and governance/audit considerations.

Preferred qualifications, capabilities, and skills

• AWS Certification(s) - and/or Working Knowledge




• AI Certifications(s) - and/or Working Knowledge