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.