Software Engineer

Lead Software EngineerWe have an exciting and rewarding opportunity for you to take your software engineering career to the next level.As a Lead Software Engineer at JPMorgan Chase 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 ResponsibilitiesDesign 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 PostgresUtilizes AI agents (CoPilot, Claude) to improve quality and delivery timelinesRequired Qualifications, Capabilities, and SkillsFormal training or certification in Software Engineering and 5+ years applied experienceStrong 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 SkillsCloud certification in AWS, GCP, or Azure.Working knowledge of Python (for AI/ML integrations) a plusExperience with multi region service deployments and zero downtime deploymentFamiliarity with Docker, Kubernetes, Helm, and modern CI/CD practices.Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputsStrong communication skills and a proactive approach to continuous improvement.Track record delivering scalable, reliable, and secure products from concept to launch.Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.