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. 

Job responsibilities

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 (AI4Tech 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 

Required qualifications, capabilities, and skills

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., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) 

Curious, detail-oriented, and motivated by complex technical problems 

Preferred qualifications, capabilities, and skills

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)