Locations: Atlanta, GA
Duration: Contract
Job Description: 
The ideal candidate is a senior hands-on DevOps engineer with strong cloud, automation, Kubernetes, and CI/CD expertise, combined with practical experience applying AI, LLM agents, and MCP-based integrations to modern engineering workflows. This role will help create a secure AI-driven delivery ecosystem that accelerates software engineering velocity while maintaining strong governance, reliability, auditability, and operational control. This role is crucial to show the efficiency.x`
POSITION GENERAL DUTIES AND TASKS :
*	The Senior AI Ops / DevOps Engineer will architect, build, and manage next-generation AI-driven CI/CD and cloud operations ecosystems. This role will go beyond traditional DevOps automation by integrating LLM agents, Model Context Protocol servers, intelligent observability, and secure AI-assisted workflows into the software delivery lifecycle.
*	Architect, build, and manage AI-enabled CI/CD pipelines that improve developer productivity, code quality, release reliability, and deployment speed.
*	Design and deploy production-grade Model Context Protocol clients and servers to securely connect enterprise LLMs with engineering tools, repositories, cloud infrastructure, and observability platforms.
*	Develop custom MCP servers using Python, TypeScript, Node.js, or JavaScript to expose logs, infrastructure metrics, deployment data, and internal tools to authorized AI agents.
*	Integrate LLM agents into developer workflows to support automated code review, vulnerability detection, test generation, release validation, and infrastructure recommendations.
*	Build and maintain robust CI/CD pipelines using GitHub Actions, GitLab CI, CircleCI, ArgoCD, Jenkins, or similar tools.
*	Implement ChatOps 2.0 capabilities that allow engineers to interact with deployment pipelines, cloud environments, logs, and operational workflows using secure conversational interfaces.
*	Create safe autonomous remediation workflows for log analysis, incident triage, root-cause analysis, and infrastructure issue resolution.
*	Build guardrails that allow AI agents to generate, inspect, and safely execute Infrastructure as Code using Terraform, OpenTofu, Terragrunt, Pulumi, Crossplane, or similar tools.
*	Manage containerized workloads using Docker and Kubernetes platforms such as AWS EKS, Azure AKS, or Google GKE.
*	Integrate AI-driven observability workflows with platforms such as Datadog, Prometheus, Grafana, CloudWatch, Splunk, Dynatrace, or ELK.
*	Implement AI safety controls including role-based access control, least-privilege execution, human-in-the-loop approvals, audit logging, rollback mechanisms, and secure tool access.
*	Partner with software engineering, DevOps, SRE, security, platform, and data/AI teams to identify opportunities for intelligent automation.
*	Create reusable automation frameworks, runbooks, dashboards, documentation, and enablement materials for engineering teams.
*	Drive an “automate everything” culture by reducing manual toil and improving operational efficiency across cloud and software delivery processes.
Basic Qualifications:
*	Minimum 7+ years of experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or Infrastructure Automation.
*	Minimum 4+ years of hands-on experience designing and managing CI/CD pipelines using GitHub Actions, GitLab CI, CircleCI, Jenkins, ArgoCD, or similar platforms.
*	Minimum 3+ years of experience managing scalable cloud environments in AWS, Azure, or GCP, with strong preference for AWS.
*	Strong hands-on experience with Kubernetes, Docker, and production container orchestration platforms such as EKS, AKS, or GKE.
*	Advanced proficiency with Infrastructure as Code tools such as Terraform, OpenTofu, Terragrunt, Pulumi, CloudFormation, or Crossplane.
*	Strong programming and scripting experience using Python, TypeScript, JavaScript, Bash, or Go.
*	Practical experience working with LLM APIs such as OpenAI, Anthropic, or similar enterprise AI platforms.
*	Experience with AI orchestration or agentic frameworks such as LangChain, CrewAI, LlamaIndex, or similar tools.
*	Strong understanding of the Model Context Protocol ecosystem and experience designing or integrating MCP clients and servers.
*	Experience integrating DevSecOps controls into CI/CD pipelines, including SAST, DAST, dependency scanning, container scanning, secrets scanning, and vulnerability management.
*	Strong knowledge of secret management and security tooling such as HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or similar platforms.
*	Experience with observability, monitoring, logging, and alerting platforms such as Datadog, Prometheus, Grafana, CloudWatch, Splunk, Dynatrace, or ELK.
*	Familiarity with security and compliance frameworks such as SOC2, ISO27001, or enterprise audit control environments.
*	Ability to troubleshoot complex pipeline, infrastructure, deployment, and production issues across cloud-native environments.
Preferred / Nice to Have:
*	Experience building AI-assisted infrastructure provisioning workflows.
*	Experience implementing autonomous or semi-autonomous incident response and remediation capabilities.
*	Experience with MLOps, model deployment pipelines, model monitoring, MLflow, SageMaker, or equivalent platforms.
*	Experience implementing human-in-the-loop approval models for AI-generated operational actions.
*	Experience with policy-as-code tools such as Open Policy Agent, Sentinel, Checkov, or similar solutions.
*	Experience working in regulated industries such as banking, financial services, healthcare, or insurance.
*	Experience with GitOps operating models using ArgoCD, Flux, or similar tools.
*	AWS, Kubernetes, DevOps, Security, or AI/ML certifications are a plus.
Soft Skills & Mindset:
*	Strong “automate everything” mindset with a passion for reducing repetitive manual tasks and operational toil.
*	Security-first approach with practical skepticism of autonomous AI actions and a focus on validation, boundaries, approvals, and rollback.
*	Ability to bridge traditional software engineering, DevOps, SRE, security, and data/AI teams.
*	Strong communication skills with the ability to explain complex AI-enabled DevOps concepts to both technical and leadership audiences.
*	Collaborative educator who can help upskill engineering teams on AI-assisted delivery, secure automation, and modern DevOps practices.
*	Ownership mindset with the ability to design solutions, implement them hands-on, and support them in production.
Travel:
*	This position requires 3 days in office per the client/project requirement.
Degree:
*	Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent work experience.
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Software Engineer

Onsite Contract Position for Senior AI Ops / DevOps Engineer in Atlanta, GA
