Lead Software Engineer - Agentic AI, Java/Python

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    "ExternalDescriptionStr": "<p><span>We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.</span></p><p class=\"MsoNormal\">As a Lead Software Engineer at JPMorganChase within the Commercial and Investment Bank's Engineering and Architecture Team, you<span>&nbsp;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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.</span></p><p class=\"MsoListParagraph\" style=\"margin-left: 0.95pt;\">&nbsp;</p><p class=\"MsoListParagraph\" style=\"margin-left: 0.95pt;\"><strong>Job responsibilities</strong></p><p class=\"MsoNormal\" style=\"margin: 0in 0in 0in 0.95pt;\">&nbsp;</p><ul><li><p>Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems</p><p>&nbsp;</p></li><li>Develops secure high-quality production code, and reviews and debugs code written by others</li><li><p>Lead a local team of software engineers and applied AI/ML practitioners, driving accountability and engagement within a global organization.</p><p>&nbsp;</p></li><li>Collaborate with product and business teams to set technical vision and execute strategic roadmaps for AI-driven software solutions.</li><li>Translate business requirements into robust software and AI/ML specifications, define milestones, and ensure timely delivery using Agile methodologies.</li><li>Architect, design, and develop secure, high-quality production software systems using Java or Python, integrating AI/ML techniques such as LLMs, Generative AI, and coding assistants</li><li>Identify opportunities to automate and remediate recurring issues, improving overall system reliability.</li><li><p>Design experiments, implement algorithms, validate results, and productionize performant, scalable, and trustworthy AI/ML solutions.</p><p>&nbsp;</p></li><li>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.</li><li>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.</li></ul><p>&nbsp;</p><p class=\"MsoNormal\">&nbsp;</p><p class=\"MsoListParagraph\" style=\"margin-left: 0.95pt;\"><strong>Required qualifications, capabilities, and skills</strong></p><p class=\"MsoNormal\" style=\"margin: 0in 0in 0in 0.95pt;\">&nbsp;</p><ul><li>Formal training or certification in software engineering concepts and 5+ years applied experience</li><li>Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT)</li><li>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</li><li>Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals and coding assistants</li><li>Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade solutions</li><li>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</li><li>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</li><li>Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)</li><li>Proficiency in automation, continuous delivery (CI/CD), and cloud-native development (preferably AWS)</li><li><p>Hands-on practical experience delivering system design, application development, testing, and operational stability</p><p>&nbsp;</p><p>&nbsp;</p></li></ul><div>&nbsp;</div><div><strong>Preferred qualifications, capabilities, and skills</strong></div><div>&nbsp;</div><ul style=\"list-style-type: disc;\"><li>Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP)</li><li>AWS Certifications (Solution Architect Associate or Professional) are advantageous</li><li>In-depth knowledge of the financial services industry and their IT systems</li><li>Practical cloud native experience</li></ul>",
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