Senior Lead Software Engineer (AI, Data, Cloud)

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.