What are the responsibilities and job description for the Sr. Lead Software Engineer - AWS Data Engineer - AI/ML position at JPMorgan Chase?
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 at JPMorganChase within the Commercial and Investment Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
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.
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
- Influences peers and project decision-makers to consider the use and application of leading-edge technologies
- Adds to the team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training in software engineering concepts and 10 years of applied experience.
- Extensive experience building and operating AWS/public cloud–based applications.
- Experience with pipelines and DAGs (Directed Acyclic Graph) for data processing and/or machine learning.
- Strong Python programming skills.
- 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.
- Demonstrated proficiency in cloud and AI/ML software practices.
- Strong problem‑solving, communication, and stakeholder collaboration skills.
- Familiarity with MLOps practices and tooling.
- Experience driving adoption of AI engineering tools (e.g., GitHub Copilot) for JIRA, documentation, coding, and releases, with measurable productivity and quality gains.
Preferred qualifications, capabilities, and skills
- Experience leading a small team as tech lead and/or manager.
- Proficiency with AWS (hands-on): SageMaker, Bedrock, Glue, Redshift Serverless, DynamoDB, EventBridge, Step Functions, Lambda, ECS, EKS, Kinesis, CloudWatch
- Experience with Python, Terraform, GitHub Copilot, Airflow, Kubernetes, Docker, MLflow, Datadog, Dynatrace, MCP.
- Familiarity with JPMC platforms/tools (highly preferred): Jules/JET, GKP (Gaia Kubernetes), Fusion MLOps