Design, build, and scale cloud infrastructure for AI-driven systems in production environments. Manage Kubernetes containers, CI/CD pipelines, and MLOps workflows with a focus on Azure cloud platform. Ensure system reliability, security, and performance while collaborating with data science teams.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
🌟 About the Role
We are looking for a Senior MLOps / Cloud Engineer to design, build, and scale cloud infrastructure that powers AI-driven systems in production.
This role sits at the intersection of DevOps, Kubernetes, and machine learning platforms, enabling reliable deployment of AI/ML workloads across modern cloud environments (Azure preferred).
You will play a key role in building secure, automated, and highly scalable infrastructure to support enterprise applications and data science teams.
🔧 Key Responsibilities
☁️ Cloud & Infrastructure
- Design and maintain scalable cloud environments (Azure preferred)
- Provision infrastructure using Infrastructure-as-Code (Terraform, ARM, or Bicep)
- Ensure high availability, performance, and cost efficiency of cloud systems
⚙️ CI/CD & Automation
- Build and manage CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools
- Automate deployments and infrastructure provisioning
- Improve release processes for faster and more reliable delivery
📦 Containers & Kubernetes
- Manage containerized workloads using Docker and Kubernetes (AKS / GKE)
- Deploy and scale applications in production environments
- Optimize cluster performance and reliability
🤖 MLOps & AI Workloads
- Support deployment of AI/ML models into production
- Build and maintain environments for model lifecycle management
- Collaborate with data science teams to operationalize ML solutions
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🔗 Integration & Event-Driven Architecture
- Build and maintain integrations using:
- Azure Functions, Logic Apps, API Management
- Service Bus, Event Grid
- Enable scalable, event-driven systems
📊 Monitoring & Reliability
- Implement monitoring, logging, and alerting using:
- Azure Monitor, Log Analytics, Application Insights
- Ensure system reliability and proactive issue detection
🔐 Security & Best Practices
- Apply best practices for:
- Identity & access management
- Secrets management
- Secure networking
✅ Required Skills & Experience
- Strong experience in DevOps / MLOps / Cloud Engineering roles
- Hands-on experience with:
- Cloud platforms (Azure preferred, AWS/GCP nice to have)
- Kubernetes and container ecosystems
- Experience with Infrastructure-as-Code (Terraform, Bicep, ARM or similar)
- Knowledge of CI/CD tools and automation practices
- Experience with monitoring and observability tools
- Familiarity with event-driven architectures and integrations
- Exposure to ML/AI workloads or MLOps environments
- Strong scripting skills (Python, Bash, or PowerShell)
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💡 Nice to Have
- Experience working with production ML pipelines
- Multi-cloud environments (AWS / GCP)
- Experience in data platforms or AI-driven products
🌍 What We Offer:
- Competitive salary and benefits package
- Full remote work flexibility
- Work with cutting-edge technologies in a dynamic, collaborative team
- Independent contractor
- Access to 500+ lifelong learning courses and development opportunities
- Fast-track leadership growth paths for senior professionals
- Open-door policy with a global, inclusive team culture
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