Lead Software Engineer - Python - GenAI

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Corporate Sector – Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.

Job responsibilities

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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.

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

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

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

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

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

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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.

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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.

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

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

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

Required qualifications, capabilities, and skills

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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

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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.

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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.

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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.

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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

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Experience developing in Linux environments.

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

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Messaging expertise with Kafka, MQ, or similar platforms.

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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).

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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.

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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

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AWS Certification(s) - and/or Working Knowledge

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AI Certifications(s) - and/or Working Knowledge