ML Ops Engineer

  • Dallas, TX
  • Posted 23 hours ago | Updated 23 hours ago

Overview

On Site
$50+
Full Time

Skills

46 years of hands-on DevOps and CI/CD experience Strong proficiency in: Jenkins pipelines (declarative and scripted) GitLab CI/CD pipeline creation and maintenance Experience building and integrating MCP and A2A protocols in enterprise environments Proficiency with Docker
container orchestration
and deployment automation Strong working experience with Google Kubernetes Engine (GKE) and Kubernetes resource management Familiarity with AWS and GCP services for hosting scalable applications Experience with monitoring/logging tools (e.g.
Prometheus
Grafana
ELK
CloudWatch) Good to Have: Experience deploying or managing Generative AI models or platforms (e.g.
private GPT
RAG pipelines) Exposure to IaC tools like Terraform
Helm
or Ansible Understanding of DevSecOps principles
SAST/DAST integration
and policy automation Familiarity with service mesh (Istio/Linkerd) andAPIgateways

Job Details

Role - ML Ops Engineer

Client - Verizon

Location : NYC/NJ/Dallas/Columbus OH - 2 to 3 days hybrid

Role Overview:

We are looking for a hands-on DevOps Engineer with a strong focus on CI/CD pipeline creation and optimization, expertise in Jenkins and GitLab CI, and experience with integrating Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocols. The role also involves deploying self-hosted Generative AI tools using Docker and Google Kubernetes Engine (GKE) and supporting scalable cloud infrastructure on AWS and Google Cloud Platform.

Key Responsibilities:

Design, develop, and maintain CI/CD pipelines in Jenkins and GitLab CI/CD
Build and integrate MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication interfaces with DevOps tools to enable automated workflows
Manage containerized environments using Docker and orchestrate them with Kubernetes (GKE)
Deploy and monitor self-hosted Generative AI platforms in Kubernetes clusters
Work with developers and architects to automate code build, test, deployment, and monitoring pipelines
Optimize infrastructure and pipeline performance across multi-cloud environments (AWS & Google Cloud Platform)
Implement secure, resilient, and scalable infrastructure-as-code using Terraform or Google Cloud Platform Deployment Manager
Support role-based access control (RBAC), secrets management, and observability within CI/CD workflows
Monitor system health and proactively improve availability and performance
Required Skills:

4 6 years of hands-on DevOps and CI/CD experience
Strong proficiency in:
Jenkins pipelines (declarative and scripted)
GitLab CI/CD pipeline creation and maintenance
Experience building and integrating MCP and A2A protocols in enterprise environments
Proficiency with Docker, container orchestration, and deployment automation
Strong working experience with Google Kubernetes Engine (GKE) and Kubernetes resource management
Familiarity with AWS and Google Cloud Platform services for hosting scalable applications
Experience with monitoring/logging tools (e.g., Prometheus, Grafana, ELK, CloudWatch)
Good to Have:

Experience deploying or managing Generative AI models or platforms (e.g., private GPT, RAG pipelines)
Exposure to IaC tools like Terraform, Helm, or Ansible
Understanding of DevSecOps principles, SAST/DAST integration, and policy automation
Familiarity with service mesh (Istio/Linkerd) and API gateways

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