Senior Lead Software Engineer; AI, Data, Cloud

As a Senior Lead Software Engineer at JPMorgan

Chase within Corporate Technology - Chief Technology Office, you serve as a senior individual contributor and technical leader on an agile team designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You drive critical technology solutions across multiple technical areas, translate firmwide objectives into concrete technical designs, and raise the engineering bar through strong architecture, high-quality delivery, and operational rigor.

• Serves as a hands‑on technical leader, contributing production code (primarily Python) and owning end‑to‑end delivery from design through production operations.

• Leads system and platform architecture for components supporting end‑to‑end ML workflows, including data transformation patterns, feature management integration, orchestration enablement, and model serving integration.

• Designs and develops agentic AI capabilities that analyze workloads and generate optimization recommendations, including evaluation approaches, monitoring, and feedback loops required for production readiness.

• Builds and maintains reusable APIs/SDKs and reference implementations that enable consistent platform adoption and reduce duplicated effort across teams.

• Partners with data scientists, ML engineers, and product teams to clarify requirements, define technical approaches, manage dependencies, and deliver measurable outcomes.

• Ensures platform reliability, scalability, performance, and cost efficiency through SLOs, proactive monitoring, incident response participation, root‑cause analysis, and continuous improvement.

• Automates infrastructure provisioning, configuration, and CI/CD pipelines for platform services using Infrastructure as Code, promoting safe and repeatable deployments across environments.

• Drives adoption of enterprise‑authorized AI‑assisted engineering practices to improve code quality, delivery speed, and operational outcomes, establishing consistent validation standards (correctness, performance, security) and promoting reuse of effective patterns.

• Produces architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.

• Mentors engineers through technical coaching, design reviews, and pairing, contributing to a strong culture of engineering excellence.

• Contributes to software engineering communities of practice and events that explore new and emerging technologies.

• Formal training or certification on software engineering concepts and 5+ years applied experience.

• Demonstrated experience in system design, application development, testing, and operational stability for distributed systems and platform services.

• Strong hands‑on depth in Python, including building and maintaining APIs/SDKs; ability to lead design and perform high‑quality code/design reviews.

• Demonstrated experience with enterprise‑authorized AI‑assisted development tools (e.g., Git Hub Copilot, Claude Code) and experience establishing practical team norms for validation and quality (correctness, performance, security) in day‑to‑day engineering workflows.

• Experience delivering platforms involving Generative AI, including LLM orchestration patterns and agentic AI frameworks, with production‑grade evaluation and monitoring practices.

• Practical experience with AWS, containerized platforms (e.g., EKS/ECS), and Terraform (or equivalent IaC).

• Databricks experience is required, including building and operating data/ML workloads on Databricks (e.g., pipelines, notebooks/jobs, Delta/feature datasets, orchestration/integration with ML workflows).

• Comprehensive knowledge of the Software Development Life Cycle, agile delivery, CI/CD, and modern engineering quality controls.

• Strong communication skills with business‑facing partners and technical stakeholders; ability to translate strategy into execution and measurable outcomes.

• Experience building ML platforms integrating data engineering, feature management, and model serving into cohesive developer‑friendly workflows.

• Knowledge of AI/ML model integration, context…