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 - Agentic Software Development Lifecycle & AI Engineer at JPMorgan Chase within the Commercial & Investment Bank’s Market Tech – Sales, Research, and Data portfolio, you will be at the forefront of our transformation to an Agentic Software Development Lifecycle (ADLC). In this technical leadership role, you will empower engineering teams to approach an Agent First development mindset, leveraging advanced coding assistants and LLM agent frameworks such as GitHub Copilot and, Anthropic Claude Code. You will also play a key role in technical enablement, distilling complex AI concepts for diverse audiences. This is a unique opportunity to influence our engineering organization, enterprise-wide standards, and collaborate with talented engineers across the organization. All while ensuring our solutions remain robust, stable, and reliable for our customers and clients.
Job responsibilities
- Champion ADLC Adoption: Lead the advocacy, transformation and implementation of agentic SDLC practices, supporting teams in integrating AI-powered coding assistants and LLM agent frameworks into their workflows.
- Technical Enablement: Develop and deliver technical workshops, documentation, and best practices to accelerate the adoption of modern development tools and methodologies.
- Data-Driven Improvement: Analyze usage data, telemetry, and prompt logs to identify adoption blockers, inform process improvements, and measure the impact of ADLC initiatives.
- Testing & Resiliency Leadership: Drive the integration of automated testing, CI/CD, and resiliency engineering (including chaos engineering and fault injection) into the ADLC, ensuring robust and fault-tolerant solutions.
- Cross-Functional Collaboration: Partner with engineering leads, product managers, and platform teams to identify opportunities for process improvement and tool adoption.
- Feedback Loop: Gather and synthesize feedback from engineering teams to inform the evolution of ADLC standards, tooling, and enterprise guidelines.
- Technical Storytelling: Distil complex technical AI concepts for both engineers and executives, producing clear and engaging technical writing, documentation, and blog posts.
- Quality Assurance: Ensure all solutions adhere to security, compliance, and operational excellence standards, with a strong emphasis on testing and resiliency.
- Metrics & Reporting: Define and track key metrics to measure the impact of ADLC adoption and identify areas for continuous improvement.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.
- Hands-on experience in software development, with a strong background in modern SDLC practices.
- Deep experience with LLM agent frameworks and AI-native IDEs; familiarity with terminal-based tools such as Claude Code and the GitHub Copilot.
- Expertise in automated testing frameworks, CI/CD pipelines, and resiliency engineering (e.g., chaos engineering, fault injection, recovery strategies).
- Proven ability to educate, mentor, and influence engineering teams through technical enablement, workshops, and documentation.
- Strong storytelling skills with the ability to distill complex technical AI concepts for both technical and non-technical audiences; experience producing technical writing, documentation, and blog posts.
- Strong interpersonal and communication skills, with experience working in cross-functional and distributed teams.
- Ability to analyze processes, gather feedback, and drive iterative improvements in engineering practices.