Sr Lead Software Engineer(AI, Data Cloud)

JPMorganChase•Plano, TX

About The Position

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. The role focuses on building platform capabilities that support end-to-end machine-learning workflows and agentic AI. You contribute production code, primarily in Python, and own delivery from design through production operations. You partner with data scientists, ML engineers, and product teams; promote reliable and cost-efficient services; automate infrastructure and deployments; and mentor engineers while contributing to engineering communities of practice.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Demonstrated experience in system design, application development, testing, and operational stability for distributed systems and platform services.
Strong hands-on Python experience, including building and maintaining APIs and SDKs; ability to lead design and conduct high-quality code and design reviews.
Experience with enterprise-authorized AI-assisted development tools, such as GitHub Copilot or Claude Code, and establishing practical team norms for validation and quality 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 such as EKS or ECS, and Terraform or equivalent Infrastructure as Code.
Required Databricks experience, including building and operating data/ML workloads such as pipelines, notebooks/jobs, Delta/feature datasets, and orchestration or 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, and ability to translate strategy into execution and measurable outcomes.

Nice To Haves

Experience building ML platforms that integrate 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 and metrics tools such as CloudWatch, Dynatrace, or Datadog.
AWS certifications.

Responsibilities

Contribute production code, primarily in Python, and own end-to-end delivery from design through production operations.
Lead 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.
Design and develop agentic AI capabilities that analyze workloads and generate optimization recommendations, including production-ready evaluation approaches, monitoring, and feedback loops.
Build and maintain reusable APIs, SDKs, and reference implementations that enable consistent platform adoption and reduce duplicated effort across teams.
Partner with data scientists, ML engineers, and product teams to clarify requirements, define technical approaches, manage dependencies, and deliver measurable outcomes.
Ensure platform reliability, scalability, performance, and cost efficiency through SLOs, proactive monitoring, incident response participation, root-cause analysis, and continuous improvement.
Automate infrastructure provisioning, configuration, and CI/CD pipelines for platform services using Infrastructure as Code, promoting safe and repeatable deployments across environments.
Drive adoption of enterprise-authorized AI-assisted engineering practices, establishing consistent validation standards for correctness, performance, and security and promoting reuse of effective patterns.
Produce architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.
Mentor engineers through technical coaching, design reviews, and pairing.
Contribute to software engineering communities of practice and events exploring new and emerging technologies.