Sr Lead Software Engineer - Agentic AI solutions

Job Overview

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Sr Lead Software Engineer at JPMorgan Chase within the Community and Consumer Banking (CCB) - Engineering Tools & Productivity (ET&P) team, you will play a pivotal role in an agile team dedicated to enhancing, building, and delivering market-leading technology products that are secure, stable, and scalable. You will be a core technical contributor, designing and implementing innovative Agentic AI solutions across the Product Development Life Cycle (PDLC), leveraging advanced AI/ML techniques to boost developer efficiency and accelerate developer efficiency toolchain adoption, resulting in faster product feature rollouts with enhanced stability and quality.

• Utilize expertise in deep learning, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and agentic workflows to develop cutting-edge solutions that drive integration and synergy across CCB developer efficiency initiatives
• Provide leadership on Agentic AI initiatives, ensuring efficient resource allocation, compliance with data regulations, and risk minimization to maximize efficiency
• Lead the design and development of robust, scalable, high-performing Agentic AI solutions across PDLC, while effectively communicating technical concepts and solutions at all organizational levels
• Work closely with product management and cross-functional forums (AI4
  
  Tech CoEs & CoPs) to define roadmaps and deliverables for AI-driven developer features and capabilities
• Drive the adoption of best practices in software engineering, machine learning operations (MLOps), and data governance, ensuring compliance with data privacy and security regulations

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Solid background in Generative/Agentic AI personalization and hands‑on expertise in applying machine learning and deep learning methods across PDLC phases
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Experience with scalable model training and strong written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences
• Extensive practical experience with cloud-native applications, Object-Oriented principles, microservice-driven architecture, REST architectural style, and RESTful APIs
• Hands‑on expertise in one or more modern programming languages and cloud platforms like Java/JavaScript/Python on AWS/Pivotal Cloud Foundry/GCP/Azure using Kubernetes
• Extensive experience with machine learning and deep learning toolkits (e.g., Tensor Flow, PyTorch, Num Py, Scikit‑Learn, Pandas)
• Curious, detail-oriented, and motivated by complex technical problems

• Strong research, investigation, presentation and evaluation skills.
• In-depth knowledge of the financial services industry and their IT systems
• Expertise in training and fine‑tuning large language models (LLMs) and embedding models, including advanced knowledge in the areas of LLM operations (LLM Ops) and AI operations (AIOps)