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Lead Software Engineer - Java / Python / AWS / AI/ML

JPMorgan Chase
Plano, USA
Lead · 5+ years experience
Discovered 3 weeks ago
awsazuredockerecseksgcp

Full Job Posting

  • Design and develop creative full-stack software solutions using innovative approaches.
  • Build and implement 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
  • 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).
  • Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
  • Familiarity with DevOps practices and tools for continuous integration and deployment.
  • Cloud certification in AWS, GCP, or Azure.
  • Working knowledge of Python (for AI/ML based implementations) a plus
  • Experience with multi region service deployments and zero downtime deployment
  • Familiarity 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/outputs
  • 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.

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