As a Software Engineer III at JPMorganChase within the AI/ML Technology, you will be a hands-on engineer responsible for building and shipping production software with a strong focus on AI-enabled capabilities. You will work across the full software development lifecycle—from requirements clarification and design through implementation, testing, deployment, and production support. You will develop backend services in Python, build APIs and microservices, implement LLM-based solutions including agentic workflows, and deliver into a multi-cloud environment using Terraform, Kubernetes, and CI/CD pipelines. This role offers you the opportunity to grow your expertise in applied AI engineering while contributing to systems that operate at enterprise scale.
Key Skills for This Role
PythonTerraformKubernetesCI/CDRESTgRPC
Full Job Posting
Role Overview
As a Software Engineer III at JPMorganChase within the AI/ML Technology, you will be a hands-on engineer responsible for building and shipping production software with a strong focus on AI-enabled capabilities. You will work across the full software development lifecycle—from requirements clarification and design through implementation, testing, deployment, and production support. You will develop backend services in Python, build APIs and microservices, implement LLM-based solutions including agentic workflows, and deliver into a multi-cloud environment using Terraform, Kubernetes, and CI/CD pipelines. This role offers you the opportunity to grow your expertise in applied AI engineering while contributing to systems that operate at enterprise scale.
Key responsibilities
Deliver software through a disciplined software development lifecycle, working from well-defined requirements through design, implementation, testing, release, and production support
Write maintainable Python code with unit and integration tests, debugging issues across application, API, data-access, and runtime layers
Implement LLM-driven workflows including prompting, tool and function calling, routing, orchestration, and state handling to support multi-step agentic task execution
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Build and maintain inference-time integrations such as model gateways, APIs, caching, fallbacks, timeouts, and concurrency controls for production AI systems
Implement retrieval-augmented generation components where applicable, including chunking, embeddings, retrieval, and grounding strategies
Build REST and gRPC endpoints following agreed contracts, implementing authentication and authorization integration, input validation, error handling, and secure data handling practices
Implement SQL-backed business logic powering APIs and microservices, including joins, aggregations, filtering, pagination, and transactional workflows
Contribute to CI/CD pipelines and Git workflows, packaging and deploying services using containers and Kubernetes with support for safe rollouts and rollbacks
Contribute to infrastructure-as-code using Terraform within established team patterns across modules, environments, and state management Improve operability of services by adding and using observability tooling including logs, metrics, traces, dashboards, and alerts, and participate in incident response and root-cause analysis
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
Apply 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
Required skills & experience
Formal training or certification on software engineering concepts and proficient applied experience
Strong hands-on Python development experience building backend services, including testing, packaging, dependency management, and maintainability
Strong database and SQL proficiency, with experience implementing application logic and APIs on top of relational data
Experience building APIs and microservices using REST or gRPC, including contracts, security basics, and observability
Practical experience delivering LLM-based features as part of software systems, with familiarity with agentic patterns
Working knowledge of delivery and operations including CI/CD, Git, containers, and Kubernetes
Familiarity with Terraform and cloud infrastructure concepts in a multi-cloud environment
Solid understanding of software engineering fundamentals and software development lifecycle practices including design, reviews, testing, release, and production support
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Preferred skills
Strong debugging and troubleshooting skills in distributed systems, including root-cause analysis and performance bottleneck identification using logs, metrics, and traces
Experience improving code quality and reliability through test strategy improvements, refactoring, static analysis, and dependency hygiene
Experience with deployment and operational best practices including safe releases, rollbacks, environment configuration, and incident readiness
Familiarity with common architecture patterns such as event-driven designs, async processing, caching, API versioning, and backward compatibility
Experience collaborating effectively in agile delivery, including estimating, breaking down work, documenting decisions, and communicating risks
About J.P. Morgan
Parent group profile
JPMorgan Chase
Banking323028 employees
Global financial services and investment banking firm.
