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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AWS Engineer - ML - **Company:** JPMorgan Chase & Co. - **Location:** Houston, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, JIRA, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Amazon DynamoDB, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Software Engineering, Datadog, Data Processing, GitHub Copilot, State Machines, Servicebus, Kubernetes, Production Code, Machine Learning Operations, Functional Programming, Cloudwatch, Terraform, Dynatrace, Serverless Computing, Docker - **Published:** June 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e808d31f6fa032cf ## About the Role Do you have experience in Tooling?, * 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 ## Description 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., * 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, J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Got AI ideas but no money? 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