What are the responsibilities and job description for the Senior AWS Engineer - 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 Data 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 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
- 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.
- Strong Python programming skills.
- Proven hands‑on delivery across system design, application development, testing, and operational stability.
- Proficiency in automation, CI/CD, and all aspects of the SDLC.
- Experience with pipelines and DAGs for data processing and/or machine learning.
- 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