Software Engineer III – DevOps & AWS

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III – DevOps/AWS Engineer at JPMorganChase within the Commercial & Investment Bank, particularly in the Payments Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
Design and deliver creative software solutions, including development and technical troubleshooting; think beyond conventional approaches to build solutions and break down complex technical problems.
Develop secure, high-quality production code; review and debug code written by others to ensure reliability, performance, and maintainability.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, accelerated test strategy, incident/root-cause analysis support), while enforcing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns.
Apply SDLC toolchain knowledge—including enterprise-authorized AI-assisted development and automation capabilities—to increase the value and impact of automation.
Identify recurring issues and lead efforts to eliminate root causes or automate remediation to improve operational stability of applications and systems.
Lead evaluation sessions with external vendors/startups and internal teams to assess architectural designs, technical capabilities, and fit within existing systems and information architecture.
Build scalable feature engineering and data pipelines on Databricks using data lake sources, leveraging Spark/Delta and Databricks workflows/jobs.
Required qualifications, capabilities, and skills
Formal training or certification in software engineering concepts with applied experience. Strong CI/CD and release management experience, including best practices and implementation with tools such as Jules, Jenkins, and Spinnaker.
Hands-on DevOps experience with Kubernetes and Docker, including deployment automation and containerized application development.
Deep AWS platform experience across compute, storage, networking, security, provisioning, and automation. Hands-on with services including EC2, ECS, EKS, ELB, S3, Lambda, API Gateway, Glue, EMR, ElastiCache, CloudWatch, and CodeDeploy.
Infrastructure provisioning expertise using Terraform (advanced techniques), including implementing/configuring AWS RDS and Aurora via Terraform.
Administer, optimize, and support Linux-based environments; ensure high availability (“always on”) for production, CERT, and testing environments.
Database experience with PostgreSQL (including RDS/Aurora) and MongoDB; ability to write automation/operations scripts in Python and Bash. Databricks experience including Spark, Delta, notebooks, workflows/jobs, and performance tuning; familiarity with GraphQL.
Provide technical support during software development, including debugging environment-specific issues and partnering with development teams to resolve them. Maintain and monitor asset inventory across environments.
Monitor, troubleshoot, and remediate issues using Splunk and observability/monitoring platforms such as Datadog, Dynatrace, or Grafana. Support cost rationalization efforts; partner with architects to gather, clarify, and translate technical requirements.
Proficient with engineering workflow tools such as Git/BitBucket, Jira, ServiceNow, and pre-commit hooks.
Strong programming skills in Python and Java, including experience with distributed data processing.
Leverage enterprise-authorized AI coding assistant tools to improve code quality and delivery speed (e.g., code generation/refactoring, unit tests, documentation), while validating outputs via peer review, automated testing, and secure coding standards; contribute reusable patterns and learnings to improve team effectiveness.
Preferred qualifications, capabilities, and skills
Exposure to cloud platforms (AWS, Azure, and/or GCP).
Exposure to large language models (LLMs) such as ChatGPT and Google Bard (or similar).
Exposure to LLM application patterns and tooling, including LangChain, retrieval-augmented generation (RAG), and fine-tuning.
Experience communicating with and engaging senior leadership and other stakeholders.
Preferred: experience building and supporting end-to-end AWS applications, including batch and streaming workloads.
Familiarity with APIs and microservices frameworks, container technologies, and workflow/orchestration tools.