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# Lead Software Engineer - AWS, Python, AI/ML

- **Company:** JPMorgan Chase & Co.
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Java (Programming Language), Artificial Intelligence, Amazon Web Services, Software Applications, Automation of Tests, Business Software, Cloud Computing, Configuration Management, Software Quality, Code Review, Continuous Integration, Data Structures, Python (Programming Language), Machine Learning, Natural Language Processing, Systems Development Life Cycle, Software Tools, Cloud Services, Tensorflow, Secure Coding, Software Construction, Software Engineering, Software Systems, Strategies of Testing, Toolchain, Enterprise Software Applications, Pytorch, Large Language Models, Generative AI, Scikit Learn, Information Technology, Code Restructuring, GPT, Programming Languages
- **Published:** September 4, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3377313447&tx=KR6161FFI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Formal training or certification in software engineering concepts and 5+ years applied experience
 * experience building AWS-based service engineering, provisioning, and automation solutions.
 * Hands-on experience designing and delivering cloud-native systems (infrastructure + applications), including IaC-driven provisioning, CI/CD, testing, observability, and operational stability (SRE/operations mindset).
 * Proficient in one or more modern programming languages used for automation and cloud services, including developing agentic AI integrations where applicable.
 * Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT)
 * Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals and coding assistants
 * Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions
 * Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
 * Advanced proficiency in Java or Python for software system development; strong grasp of software engineering best practices, system design, application development, testing, and operational stability
 * 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

Preferred qualifications, capabilities, and skills

 * Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP)
 * AWS Certifications (Solution Architect Associate or Professional) are advantageous
 * In-depth knowledge of the financial services industry and their IT systems
 * Practical cloud native experience

## Description

* Designs, builds, and operates AWS-native service engineering solutions, including automated provisioning, configuration management, and lifecycle orchestration (IaC, CI/CD, self-service workflows), applying creative problem-solving to break down complex distributed-systems issues beyond conventional approaches.
 * Develops secure, resilient, production-grade automation and agentic AI solutions (LLM-based agents integrated with cloud services and enterprise systems), maintaining high-quality code, algorithms, and event-driven/run-time workflows that operate reliably with dependent platforms and control planes.
 * 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.
 * Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
 * Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
 * Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
 * Adds to team culture of diversity, opportunity, inclusion, and respect


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