Sr Lead Software Engineer - AI/ML

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 - ML at at JPMorgan Chase within the Consumer & Community Banking (CCB) line of business, you serve as a seasoned member of an agile team focused on building, scaling, and maintaining robust machine learning platforms. You will design and deliver trusted, market-leading infrastructure and tools that empower data scientists and ML engineers to develop, deploy, and monitor models efficiently and securely. You are responsible for implementing critical technology solutions across multiple technical areas to support the firm’s business objectives and drive innovation in ML platform capabilities.

Job responsibilities

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Serves as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle

Required qualifications, capabilities, and skills

• A. Formal training or certification on software engineering concepts and 5+ years applied experience ( NAMR/APAC – India/ LATAM/ Hong Kong)
  B. Formal training or certification on software engineering concepts and advanced applied experience (EMEA/LATAM-Brazil)
  C. Singapore follow local country guidance
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL). Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure
• Strong understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring
• Experience developing APIs and platform services for ML workflow
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
• Ability to tackle design and functionality problems independently with little to no oversight
  Practical cloud native experience

Preferred qualifications, capabilities, and skills

• Familiarity with Databricks for scalable data engineering and ML platform integration
• Experience working with Snowflake for cloud-based data warehousing and analytics
• Exposure to Snorkel AI for programmatic data labeling and training data management
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)
• Familiarity with feature stores, model registries, and ML metadata management
• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)
• Experience with RESTful APIs and microservices architectures