Cloud and AI Ops Engineer

Hewlett Packard Enterprise•San Jose, CA

2d•$105,500 - $213,500•Onsite

About The Position

As part of HPE Networking business unit, this is a fantastic opportunity for someone who is looking for new & exciting opportunities to make a difference! You will be joining HPE to provide top class Cloud Services to meet the fast-growing business requirements of Secure Access Service Edge (SASE) business. The Cloud and AI Engineer builds from the ground up to meet the needs of mission-critical applications and is always looking for innovative approaches to deliver end-to-end technical solutions to solve customer problems. This role will be on-site 2+ days a week in San Jose

Requirements

3+ years programming experience in Python is a must.
3+ years of experience in developing Cloud native applications, Kubernetes, and container environments is a must.
Expertise in automation and CI-CD pipeline tools like Terraform, Ansible, Jenkins, and/or Git is a must.
Expertise in monitoring tools like Grafana, Datadog, Prometheus, Observe, or Splunk is a must.
Experience in developing, deploying, and maintaining applications for Public Cloud environments (AWS, Azure, GCP, etc).
Familiarity with Data analysis and basic machine learning concepts.
Knowledge of networking protocols and concepts such as routing, TCP/IP, BGP, OSPF/ISIS, NetFlow, SNMP, and Internet Traffic Engineering techniques.
Good communication skills, written and verbal, along with ability to communicate complex procedures.
A desire to constantly grow and learn new skills. In this team you will be exposed to new technologies and new problems. Ability to assimilate new ideas and tackle tough problems as they arise will be the key to success.
Bachelor's or Master’s degree in Computer Science, Information Systems, or equivalent.

Nice To Haves

Knowledge of NoSQL databases like MongoDB, DynamoDB is preferred.

Responsibilities

Deployment of Cloud infrastructure using Docker Containerization.
Automate Cloud Orchestration Services using Kubernetes, Python, and Jenkins.
Use NoSQL Databases like MongoDB, DynamoDB to support Cloud based applications.
Develop and Deploy Lambda functions using AWS-SAM.
Monitor and generate alerts using Distributed Log and Observability utilities like Grafana, Influx, or Kibana.
Leverage Infrastructure-as-Code frameworks like Terraform to deploy Cloud Services.
Integrate Operational data sources like logs, metrics, alerts, and telemetry sources into AIOps platforms.
Enable SRE support and monitoring for HPE Networking SASE products to ensure that applications are running as per their requirements.
Create strategies to detect issues, address those issues, and design systems to troubleshoot automatically using AI.