We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorgan Chase within the Asset and Wealth Management Technology and International Private Bank's Artificial Intelligence and Machine Learning Team, you will build and operate the platform, pipelines, and tooling that our agentic AI and machine learning products run on. Your role will involve working under the direction of the team’s Senior Platform Engineer, helping to deploy, monitor, and maintain reliable and secure production AI/ML services (including PACE).
Job responsibilities
- Builds and maintains deployment pipelines, containers, and infrastructure-as-code for the team's AI/ML services
- Implements and operates monitoring, logging, and alerting so production services stay reliable and observable
- Supports model serving, environment management, and release automation
- Applies platform security and access controls to firm-wide standards
- Participates in on-call / production support and incident response as the team's ownership model matures
- Contributes to agile ceremonies, code reviews, and technical design discussions, growing towards greater technical ownership
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Uses AI coding tools (e.g., Claude Code, GitHub Copilot) as part of day-to-day development
- Adds to the team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Proficiency in Python and familiarity with modern software engineering practices (testing, version control, code review)
- Working knowledge of containerization (Docker) and Kubernetes concepts
- Experience with CI/CD pipelines and cloud-native deployment
- Understanding of monitoring and observability concepts
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
- Ability to communicate clearly with teammates and stakeholders
Certified in Industry-recognized container / Kubernetes certification (e.g., Certified Kubernetes Application Developer (CKAD), or similar)
Preferred qualifications, capabilities, and skills
- Exposure to MLOps or serving ML models in production
- Familiarity with infrastructure-as-code tooling (e.g., Terraform)
Exposure to financial services technology
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