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 Senior Lead Software Engineer at JPMorganChase within the AI/ML Data Platforms team, you will be a key member of an agile team responsible for enhancing, building, and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will drive meaningful business impact through hands-on engineering leadership, applying deep technical expertise and structured problem-solving to address complex challenges across multiple technologies and applications. In this role, you will help design and deliver agentic AI platforms and large language model (LLM)-enabled services for enterprise use cases. You will contribute to architecture and engineering decisions, build cloud-native services on AWS, and improve system quality through strong evaluation, observability, and operational excellence practices. You will also raise engineering standards through high-quality code reviews, clear documentation, and effective collaboration across teams. Job responsibilities Provide technical guidance and direction to business and engineering teams by partnering with external teams to align on priorities, unblock delivery, and drive successful engineering outcomes. Develop secure, high-quality production code and lead code reviews; review, debug, and improve code written by others to raise overall engineering quality. Drive architecture and design decisions that influence product design, application functionality, and technical operations (including SDLC practices). Serve as a subject matter expert in one or more focus areas, helping teams make sound technical trade-offs and resolve complex problems. Evaluate and introduce leading-edge technologies where appropriate, influencing peers and decision-makers with clear rationale and risk/benefit analysis. Build and operate production-grade LLM applications, including agentic patterns and tool integrations for enterprise use cases. Design and deliver cloud-native services on AWS using containers and serverless architectures, with strong attention to scalability and operational resilience. Implement retrieval-augmented generation (RAG) solutions, including embeddings, semantic search, and practical context engineering to improve answer quality and control. Build reliable service APIs and integrations with a focus on security, performance, and maintainability. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the

SDLC/TLM

toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Required Qualifications, Capabilities, and Skills:

Formal training or certification on software engineering concepts and 5+ years applied experience Strong Python engineering skills; experience with PyTorch or TensorFlow Expertise working with Vector storage systems and designing memory for Agents Expertise developing long running agents that run autonomously using tools, skills and human in the loop Proven experience deploying LLM-backed services to production (APIs, microservices) Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization Demonstrated commitment to responsible AI practices and operational excellence Strong communication and collaboration skills, working across product, risk, legal, and compliance teams Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data. Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.

Preferred Qualifications, Capabilities, and Skills:

Experience with fine-tuning, adapters, or custom evaluation frameworks. Background operating AI systems in regulated environments (finance, healthcare, etc.). Experience with prompt engineering and LLM orchestration. Knowledge of safety filters, audit logging, and explainability in production systems. Experience mentoring senior engineers and leading architecture discussions. Demonstrated ability to influence technical roadmaps and priorities.