

Maxonic
AI-Ops Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI-Ops Engineer on a contract basis in Stanford, CA, offering $60/hour based on experience. Requires 3+ years in DevOps and 2+ years with AWS. Key skills include Python, AIOps, and Infrastructure as Code expertise.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
480
-
ποΈ - Date
December 20, 2025
π - Duration
Unknown
-
ποΈ - Location
Hybrid
-
π - Contract
Unknown
-
π - Security
Unknown
-
π - Location detailed
California, United States
-
π§ - Skills detailed
#API (Application Programming Interface) #VPC (Virtual Private Cloud) #Docker #Infrastructure as Code (IaC) #Bash #Prometheus #Observability #Splunk #Code Reviews #Grafana #Lambda (AWS Lambda) #Version Control #IAM (Identity and Access Management) #S3 (Amazon Simple Storage Service) #Kubernetes #ML (Machine Learning) #Compliance #GIT #AI (Artificial Intelligence) #AWS (Amazon Web Services) #Debugging #Agile #Scala #GDPR (General Data Protection Regulation) #Security #SageMaker #Automation #GitLab #Jenkins #GitHub #TypeScript #EC2 #Cloud #Python #Monitoring #Anomaly Detection #Deployment #Terraform #Datadog #NLP (Natural Language Processing) #Computer Science #Containers #DevOps
Role description
Maxonic maintains a close and long-term relationship with our direct client. In support of their needs, we are looking for a AI-Ops Engineer
Job Description:
Job Title: AI-Ops Engineer
Job Type: Contract
Job Location: Stanford, CA
Work Schedule: On-site
Rate: $60,Based on experience
Position Overview
The AI-Ops Engineer is a key technical contributor responsible for evolving traditional DevOps into AI- Ops at CGOE. This role leverages AI and machine learning to automate and enhance IT operations
βincluding performance monitoring, anomaly detection, root cause analysis, and automated
remediation.
Working at the intersection of cloud infrastructure, AI-driven automation, and operational excellence, the engineer embeds intelligence into infrastructure, deployment, and monitoring to ensure high availability, predictive issue resolution, and operational efficiency across CGOE's global online programs.
Key Responsibilities
AI-Driven Operations & Automation
Implement AIOps solutions that use ML algorithms to automate performance monitoring, workload scheduling, and infrastructure management.
Build anomaly detection systems that identify infrastructure issues before they impact users.
Develop automated root cause analysis capabilities using ML to correlate events and filter noise from critical alerts.
Create predictive maintenance workflows that analyze historical patterns to proactively mitigate issues.
Design and implement automated remediation scripts that respond to incidents without human intervention.
Observability & Intelligent Monitoring
Architect comprehensive observability platforms that aggregate data from disparate sources into unified dashboards.
Implement intelligent alerting systems using NLP and ML to reduce alert fatigue and surface actionable insights.
Build real-time analytics dashboards for coordinated diagnosis across teams.
Deploy application performance monitoring (APM) solutions integrated with AI-driven analytics. Ensure end-to-end visibility across cloud infrastructure, applications, and AI/ML workloads.
Cloud Infrastructure & DevOps
Design, build, and maintain scalable, secure AWS infrastructure using Infrastructure as Code (CloudFormation, Terraform, or CDK).
Implement and manage containerized environments using Docker, AWS ECS, Fargate, and Kubernetes (EKS).
Build CI/CD pipelines for continuous delivery, integrating AI-powered code quality and deployment optimization.
Manage cloud automation and optimization to improve cost-efficiency and resource utilization.
Ensure compliance with company and regulatory standards (FERPA, GDPR) for secure data handling and governance.
Collaboration & Continuous Improvement
Partner with cross-functional teams to implement domain-agnostic AIOps solutions across the organization.
Use Git-based version control and code review best practices as part of a collaborative, agile workflow.
Document operational procedures, runbooks, and AIOps workflows for team knowledge sharing.
Continuously evaluate and adopt emerging AIOps tools, AWS services, and AI-driven automation technologies.
Contribute to building an AI-first operational culture that prioritizes automation and predictive capabilities.
Required Qualifications
Education & Certifications
Bachelor's degree in Computer Science, DevOps, Cloud Engineering, or a related field (Master's preferred).
AWS certification preferred (Solutions Architect, SysOps Administrator, or DevOps Engineer); Professional-level certification a plus.
Experience
3+ years of experience in DevOps, SRE, or Cloud Engineering roles.
2+ years of hands-on experience with AWS infrastructure (EC2, ECS, Lambda, S3, IAM, VPC). Experience implementing monitoring, observability, and alerting solutions at scale.
Familiarity with ML/AI concepts and their application to operational automation.
Technical Skills
Languages: Python (required); Bash, Go, or TypeScript preferred.
AIOps & Monitoring: CloudWatch, X-Ray, Prometheus, Grafana, Datadog, or Splunk with ML capabilities.
Infrastructure as Code: AWS CloudFormation, Terraform, or AWS CDK.
Containers & Orchestration: Docker, AWS ECS/Fargate, Kubernetes (EKS).
AWS Services: Lambda, EC2, S3, API Gateway, EventBridge, CloudWatch, IAM, VPC, CodePipeline, SageMaker.
CI/CD Tools: GitHub Actions, AWS CodePipeline, Jenkins, or GitLab CI.
Data & Analytics: Experience with log aggregation, metrics analysis, and event correlation platforms.
Desired Attributes
Strong understanding of AIOps principlesβusing AI to enhance, not just support, IT operations. Passion for automation and eliminating manual, repetitive operational tasks.
Excellent problem-solving, debugging, and root cause analysis skills.
Demonstrated ability to learn rapidly, adapt to new technologies, and continuously improve. Strong communication skills with ability to collaborate across technical and non-technical teams. Commitment to reliability, security, and operational excellence.
Thrives in a fast-paced, evolving environment, proactively seeking opportunities to embed intelligence into systems and processes.
Success Metrics
Reduced mean time to detection (MTTD) and mean time to resolution (MTTR) through AI- driven automation.
High system uptime and availability (99.9%+) for critical infrastructure.
Decreased alert noise and improved signal-to-noise ratio in monitoring systems.
Measurable cost savings through predictive optimization and automated remediation. Consistent adoption of AIOps best practices across the organization.
Working Conditions
Hybrid work model (2-3 days on campus).
Collaborative, agile team culture with regular code reviews and paired development. Occasional on-call responsibilities for critical infrastructure.
About Maxonic:
Since 2002 Maxonic has been at the forefront of connecting candidate strengths to client challenges. Our award winning, dedicated team of recruiting professionals are specialized by technology, are great listeners, and will seek to find a position that meets the long-term career needs of our candidates. We take pride in the over 10,000 candidates that we have placed, and the repeat business that we earn from our satisfied clients.
Interested in Applying?
Please apply with your most current resume. Feel free to contact Jaspreet Singh (Jaspreet.s@maxonic.com/ (510) 613-4990) for more details
Maxonic maintains a close and long-term relationship with our direct client. In support of their needs, we are looking for a AI-Ops Engineer
Job Description:
Job Title: AI-Ops Engineer
Job Type: Contract
Job Location: Stanford, CA
Work Schedule: On-site
Rate: $60,Based on experience
Position Overview
The AI-Ops Engineer is a key technical contributor responsible for evolving traditional DevOps into AI- Ops at CGOE. This role leverages AI and machine learning to automate and enhance IT operations
βincluding performance monitoring, anomaly detection, root cause analysis, and automated
remediation.
Working at the intersection of cloud infrastructure, AI-driven automation, and operational excellence, the engineer embeds intelligence into infrastructure, deployment, and monitoring to ensure high availability, predictive issue resolution, and operational efficiency across CGOE's global online programs.
Key Responsibilities
AI-Driven Operations & Automation
Implement AIOps solutions that use ML algorithms to automate performance monitoring, workload scheduling, and infrastructure management.
Build anomaly detection systems that identify infrastructure issues before they impact users.
Develop automated root cause analysis capabilities using ML to correlate events and filter noise from critical alerts.
Create predictive maintenance workflows that analyze historical patterns to proactively mitigate issues.
Design and implement automated remediation scripts that respond to incidents without human intervention.
Observability & Intelligent Monitoring
Architect comprehensive observability platforms that aggregate data from disparate sources into unified dashboards.
Implement intelligent alerting systems using NLP and ML to reduce alert fatigue and surface actionable insights.
Build real-time analytics dashboards for coordinated diagnosis across teams.
Deploy application performance monitoring (APM) solutions integrated with AI-driven analytics. Ensure end-to-end visibility across cloud infrastructure, applications, and AI/ML workloads.
Cloud Infrastructure & DevOps
Design, build, and maintain scalable, secure AWS infrastructure using Infrastructure as Code (CloudFormation, Terraform, or CDK).
Implement and manage containerized environments using Docker, AWS ECS, Fargate, and Kubernetes (EKS).
Build CI/CD pipelines for continuous delivery, integrating AI-powered code quality and deployment optimization.
Manage cloud automation and optimization to improve cost-efficiency and resource utilization.
Ensure compliance with company and regulatory standards (FERPA, GDPR) for secure data handling and governance.
Collaboration & Continuous Improvement
Partner with cross-functional teams to implement domain-agnostic AIOps solutions across the organization.
Use Git-based version control and code review best practices as part of a collaborative, agile workflow.
Document operational procedures, runbooks, and AIOps workflows for team knowledge sharing.
Continuously evaluate and adopt emerging AIOps tools, AWS services, and AI-driven automation technologies.
Contribute to building an AI-first operational culture that prioritizes automation and predictive capabilities.
Required Qualifications
Education & Certifications
Bachelor's degree in Computer Science, DevOps, Cloud Engineering, or a related field (Master's preferred).
AWS certification preferred (Solutions Architect, SysOps Administrator, or DevOps Engineer); Professional-level certification a plus.
Experience
3+ years of experience in DevOps, SRE, or Cloud Engineering roles.
2+ years of hands-on experience with AWS infrastructure (EC2, ECS, Lambda, S3, IAM, VPC). Experience implementing monitoring, observability, and alerting solutions at scale.
Familiarity with ML/AI concepts and their application to operational automation.
Technical Skills
Languages: Python (required); Bash, Go, or TypeScript preferred.
AIOps & Monitoring: CloudWatch, X-Ray, Prometheus, Grafana, Datadog, or Splunk with ML capabilities.
Infrastructure as Code: AWS CloudFormation, Terraform, or AWS CDK.
Containers & Orchestration: Docker, AWS ECS/Fargate, Kubernetes (EKS).
AWS Services: Lambda, EC2, S3, API Gateway, EventBridge, CloudWatch, IAM, VPC, CodePipeline, SageMaker.
CI/CD Tools: GitHub Actions, AWS CodePipeline, Jenkins, or GitLab CI.
Data & Analytics: Experience with log aggregation, metrics analysis, and event correlation platforms.
Desired Attributes
Strong understanding of AIOps principlesβusing AI to enhance, not just support, IT operations. Passion for automation and eliminating manual, repetitive operational tasks.
Excellent problem-solving, debugging, and root cause analysis skills.
Demonstrated ability to learn rapidly, adapt to new technologies, and continuously improve. Strong communication skills with ability to collaborate across technical and non-technical teams. Commitment to reliability, security, and operational excellence.
Thrives in a fast-paced, evolving environment, proactively seeking opportunities to embed intelligence into systems and processes.
Success Metrics
Reduced mean time to detection (MTTD) and mean time to resolution (MTTR) through AI- driven automation.
High system uptime and availability (99.9%+) for critical infrastructure.
Decreased alert noise and improved signal-to-noise ratio in monitoring systems.
Measurable cost savings through predictive optimization and automated remediation. Consistent adoption of AIOps best practices across the organization.
Working Conditions
Hybrid work model (2-3 days on campus).
Collaborative, agile team culture with regular code reviews and paired development. Occasional on-call responsibilities for critical infrastructure.
About Maxonic:
Since 2002 Maxonic has been at the forefront of connecting candidate strengths to client challenges. Our award winning, dedicated team of recruiting professionals are specialized by technology, are great listeners, and will seek to find a position that meets the long-term career needs of our candidates. We take pride in the over 10,000 candidates that we have placed, and the repeat business that we earn from our satisfied clients.
Interested in Applying?
Please apply with your most current resume. Feel free to contact Jaspreet Singh (Jaspreet.s@maxonic.com/ (510) 613-4990) for more details






