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Infrastructure Engineer III-AWS, Terraform, Python-ML Ops
senior · Technology / Software Development
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level
As an Infrastructure Engineer III at JPMorgan Chase within the Infrastructure Platforms team, you develop knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Through this work you begin to apply your proficiency in a single application or technical methodology.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Build and support Environment-as-Code solutions and core engineering workstreams using AWS (EC2, EKS, CloudFront), Terraform, OpenSearch, EMR, and Python.
- Drive new initiatives for cloud migration and platform modernization leveraging AWS, Terraform, and Python.
- Provision and operate EKS and ECS clusters, and implement observability with Datadog, Dynatrace, and Gremlin.
- Troubleshoot technical issues by decomposing problems and resolving cases of basic complexity.
- Maintain an up-to-date inventory of environments and assist development teams in debugging environment-specific issues.
- Deliver BAU UNIX support, remediate risk breaks, and enforce audit compliance across global operations.
- Manage and distribute workloads across APAC and NA to ensure continuous coverage and operational efficiency.
- Collaborate with cross-functional partners to proactively meet new opportunities and project demands.
- Uphold high standards for service delivery, regulatory compliance, and customer satisfaction.
- Contribute to sustained excellence in support, engineering, and feature onboarding activities.
- Document configurations, runbooks, and operational procedures to improve supportability and knowledge sharing.
- 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.
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 7+ years applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Hand on Experience in AWS Terraform services for EKS,EC2, Route-53, ALB, NLB , SQS, Auto scaling, HPA and all other AWS services
- Expert Infrastructure as Code with Terraform (modules, state backends, workspaces, CI integration, policy controls). Strong AWS/public cloud knowledge (VPC, ALB/NLB, ECR/EKS, IAM, KMS, CloudWatch/CloudTrail) and cloud networking fundamentals
- Build and support web environments on AWS, including EC2, ELB, RDS, Lambda, and S3.
- Automate infrastructure tasks using Python or another scripting language.
- Demonstrate understanding of compliance, risk management, and audit processes
- Experience in Java, Python, Scala or C++, Cassandra or equivalent no-SQL databases
- Experience in partnering with product and program management teams
- Experience in building scalable systems based on web services
- Ability to lead the design of large-scale systems and to reason about the challenges and tradeoffs
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Demonstrable ability to code in one or more languages. Experience across the whole Software Development Life Cycle
- Deep experience with CI/CD (e.g., Jenkins, Spinnaker/Argo), artifact management, and automated testing strategies. Containerization & DevOps: expert skills in Kubernetes (K8s), Docker, Helm, GitOps, and CI/CD pipelines (Jenkins, GitLab CI)
- Practical experience applying agentic AI/LLM capabilities to DevSecOps use cases (e.g., assisted troubleshooting, code/IaC generation with review, runbook automation) with attention to accuracy, guardrails, and auditability
- Data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
- Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) and deploying/managing ML models
- Experience deploying models using Canary, Blue/Green, or Shadow deployment strategies, with understanding of data versioning and ML model lifecycle management
- Monitoring & Reliability: experience setting up monitoring for both infrastructure and models (drift detection, model accuracy) using Prometheus/Grafana
- Strong knowledge of AWS, Azure, or GCP (including serverless architectures, storage solutions, and network configuration), plus Postgres experience and work in a highly regulated environment/industry