AI Operations Engineer

Systems Integration Specialist

AI Ops Engineer (4+ Years Experience) What you’ll do As an AI Ops Engineer, you will design, deploy, operate, and optimize enterprise-grade AI-powered applications and intelligent agents on Azure that support business workflows and customer interactions at scale. You will operationalize AI/ML models and LLM-powered applications by managing deployment pipelines, monitoring performance, ensuring reliability, and maintaining scalability in production environments. This is a hands-on engineering role focused on operational excellence, where you will work closely with engineering, product, and CX teams to ensure AI systems run efficiently in production. You will leverage Azure services such as Azure OpenAI, Azure Machine Learning, Cognitive Services, Kubernetes, and DevOps pipelines to operationalize AI workloads, continuously monitor model performance, improve latency and accuracy, and ensure governance, security, and system stability. What you’ll bring • 4+ years of hands-on software engineering, cloud, or platform engineering experience • Strong experience operationalizing AI/ML or GenAI applications in production environments • Proven expertise with Microsoft Azure cloud platform, especially AI/ML services • Experience with CI/CD pipelines, infrastructure automation, and cloud deployments • Strong troubleshooting, monitoring, and production reliability experience • Ability to independently manage AI deployments end-to-end • Degree in Computer Science, Engineering, Data Science, or equivalent practical experience Skill Requirements • Experience deploying and managing AI/ML and LLM-based applications in production • Hands-on experience with Azure OpenAI, Azure Machine Learning, Azure AI Studio, and Cognitive Services • Knowledge of containerization and orchestration (Docker, Kubernetes, AKS) • Experience with CI/CD pipelines such as Azure DevOps or GitHub Actions • Familiarity with agentic AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI from an operational perspective • Understanding of RAG architectures, vector databases, and AI observability tools • Strong Python scripting and automation experience • Experience monitoring AI models including logging, evaluation, performance metrics, and alerting • Knowledge of MLOps/LLMOps practices including model versioning, governance, and lifecycle management • Familiarity with Git, infrastructure-as-code, and standard DevOps workflows • Strong debugging, production support, and performance optimization skills Typical AI Ops Roles and Responsibilities • Deploy AI agents and AI-powered applications to production environments • Maintain CI/CD pipelines for AI models and applications • Monitor AI system performance, reliability, and usage metrics • Troubleshoot operational issues including latency, hallucinations, or integration failures • Implement logging, observability, and evaluation frameworks for AI systems • Manage Azure infrastructure supporting AI workloads • Ensure security, compliance, and governance for AI deployments • Continuously improve system scalability, stability, and operational efficiency • Collaborate with AI engineers and product teams to operationalize new AI features

Experience Level

Mid Level