We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, Automation & AI Solutions team, you will architect, design, and deliver scalable software products that combine cloud-native microservices, and generative AI/LLM capabilities. You will own key technical decisions, drive engineering best practices, and ensure solutions are secure, reliable, and production-ready.
Job responsibilities:
- Design and develop creative full-stack software solutions using innovative approaches.
- Lead the creation and implementation of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
- 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.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
- Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
- Communicate project status clearly and manage priorities across multiple initiatives.
- Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
- Codes in Java, AWS ECS, EKS, and Postgres
- Utilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines
Required qualifications, capabilities, and skills
- Formal training or certification in Software Engineering and 5+ years applied experience
- Strong system design, application development, and operational stability skills in production environments.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
- Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
- Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
- Familiarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
- Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
- 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.
- Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
Preferred qualifications, capabilities, and skills:
- Cloud certification in AWS, GCP, or Azure.
- Working knowledge of Python (for AI/ML integrations) a plus
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Experience with multi region service deployments and zero downtime deployment
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Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
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Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs
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Strong communication skills and a proactive approach to continuous improvement.
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Track record delivering scalable, reliable, and secure products from concept to launch.
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Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.