Job Description

Job Summary

Role Overview

We are looking for a highly skilled DevOps/MLOps/LLMOps Engineer to build, automate, and manage scalable AI/ML and Generative AI platforms. The ideal candidate will have experience operationalizing ML models and LLM-based applications, implementing CI/CD pipelines, monitoring AI systems, and enabling production-grade AI deployments.

Key Responsibilities

• Design and maintain CI/CD pipelines for AI/ML and Generative AI applications.
• Build and manage MLOps and LLMOps platforms for model training, deployment, monitoring, and governance.
• Automate provisioning and deployment using Infrastructure as Code (IaC).
• Implement observability, performance monitoring, and cost optimization for AI workloads.
• Manage containerized workloads using Docker and Kubernetes.
• Support deployment and lifecycle management of LLMs, RAG solutions, and Agentic AI applications.
• Ensure security, compliance, and reliability of AI platforms.

Skill Requirements

• 5-8 years of experience in DevOps, MLOps, or Platform Engineering.
• Strong expertise in Azure, AWS, or GCP.
• Hands-on experience with Docker, Kubernetes, Terraform, GitHub Actions, Azure DevOps, or Jenkins.
• Experience with ML lifecycle tools such as MLflow, Kubeflow, Azure ML, or SageMaker.
• Knowledge of LLMOps, prompt management, model evaluation, vector databases, and RAG architectures.
• Experience monitoring production AI systems using tools such as Prometheus, Grafana, Azure Monitor, or dynatrace
• Strong scripting/programming skills in Python, Bash, or PowerShell.

Preferred Skills

• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and Agentic AI solutions.
• Familiarity with LangChain, LangGraph, Semantic Kernel, or AutoGen.
• Knowledge of Responsible AI, model governance, and AI security practices.
• Azure, AWS, Kubernetes, or Terraform certifications.

Ideal Candidate

A hands-on engineer with proven experience building and operating enterprise-scale DevOps, MLOps, and LLMOps platforms, enabling reliable deployment, monitoring, governance, and optimization of AI and Generative AI solutions in production environments.

Other Requirements

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