Job Description: Azure AI Engineer
Role Title: Azure AI Engineer / Senior Azure AI/ML Engineer
Experience Level: 5+ Year
Role Overview
We are looking for an experienced Azure AI Engineer with 5+ years of hands-on experience in cloud, artificial intelligence, and machine learning architectures to design, build, and operationalize enterprise-grade AI and Generative AI solutions. In this role, you will leverage Azure AI Foundry, Azure OpenAI Service, and Azure Machine Learning (Azure ML) to build scalable RAG pipelines, deploy intelligent agents, and establish production MLOps/LLMOps workflows across enterprise workloads.
Key Responsibilities
1. Generative AI & LLM Solution Delivery
- Architect and implement end-to-end GenAI applications utilizing Azure OpenAI Service (GPT-4o, reasoning models, and embedding models).
- Build advanced Retrieval-Augmented Generation (RAG) architectures integrating Azure AI Search (vector, hybrid, and semantic re-ranking).
- Develop, test, and orchestrate complex LLM workflows and multi-agent systems using frameworks like Semantic Kernel, LangChain, LlamaIndex, or Prompt Flow.
- Fine-tune, evaluate, and benchmark foundation models on domain-specific datasets to meet accuracy, latency, and cost thresholds.
2. Model Lifecycle, Platform & Azure AI Foundry
- Leverage Azure AI Foundry (formerly Azure AI Studio) to unify model exploration, prompt design, evaluation metrics, and model catalog deployments.
- Build and manage machine learning training, tuning, and batch/real-time inference pipelines in Azure Machine Learning (Azure ML).
- Containerize and deploy models as scalable endpoints using Azure ML Managed Online Endpoints, Azure Container Apps, or Azure Kubernetes Service (AKS).
- Manage model versioning, artifacts, metrics, and experimentation tracking using MLflow integrated with Azure ML.
3. LLMOps, Automation & Productionization
- Build and maintain CI/CD pipelines via Azure DevOps or GitHub Actions for automated testing, evaluation gating, and blue-green deployment of AI models and RAG endpoints.
- Embed automated LLM evaluation suites to systematically measure hallucination, groundedness, relevance, and toxicity prior to deployment.
- Monitor runtime performance, inference latency, token usage, and compute costs using Azure Monitor, Application Insights, and custom telemetry dashboards.
4. Enterprise Security, Governance & Responsible AI
- Enforce Responsible AI guidelines using Azure AI Content Safety, runtime guardrails, prompt injection defenses, and PII masking.
- Secure AI infrastructure through Azure Virtual Networks (VNets), Private Endpoints, Azure Key Vault, Managed Identities, and strict Azure RBAC.
Required Technical Skills & Qualifications
Core Competencies:
- Experience: 5+ years of overall technical experience, with at least 2–3 years focused on building, deploying, and operating AI/ML and GenAI systems on Microsoft Azure.
- Azure AI & ML Stack:
- Azure AI Foundry (project hubs, prompt evaluation, model catalog).
- Azure OpenAI Service (API integration, quota management, fine-tuning).
- Azure Machine Learning (Compute Clusters, Environments, Pipelines, Endpoints, Datasets).
- Azure AI Search (Vector search, indexing, semantic ranker).
- Programming & Frameworks:
- Advanced proficiency in Python (Pandas, NumPy, AsyncIO).
- Hands-on experience with LLM orchestration frameworks: Semantic Kernel, LangChain, or LlamaIndex.
- Exposure to classical ML/DL libraries: scikit-learn, PyTorch, or Hugging Face.
- Data & Storage: Strong SQL skills, along with experience handling Azure Blob Storage, Azure Data Lake Storage (ADLS Gen2), and Cosmos DB.
- DevOps & Infrastructure:
- Docker containerization.
- CI/CD automation via Azure DevOps or GitHub Actions.
- Basic Infrastructure as Code (IaC) using Terraform or Bicep.
Preferred Qualifications
- Certifications:
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Scientist Associate (DP-100)