As a Senior Lead Software Engineer at JPMorganChase 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.

Job responsibilities

• 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.

Required qualifications

• 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., GitHub 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.

Preferred qualifications

• Experience building ML platforms integrating data engineering, feature management, and model serving into cohesive developer-friendly workflows.
• Knowledge of AI/ML model integration, context engineering, and MCP-style patterns for tool/function integration.
• Exposure to Snowflake.
• Familiarity with observability/metrics tools (e.g., CloudWatch, Dynatrace, Datadog).
• AWS certifications.