Azure AI Infrastructure Engineer

Job Summary

We are looking for an experienced Azure AI Infrastructure Engineer to design, implement, and manage scalable AI/ML and Generative AI platforms on Microsoft Azure. This role focuses on building robust infrastructure for model training, fine-tuning, deployment, and multi-agent orchestration using Azure-native services.

Key Responsibilities

• Design and implement end-to-end AI infrastructure on Azure
• Build and manage environments using Azure Machine Learning
• Deploy and manage LLMs using Azure OpenAI Service
• Create scalable pipelines for model training, fine-tuning, and inference
• Implement MLOps pipelines (CI/CD for ML models) using Azure DevOps
• Manage GPU-enabled compute clusters for high-performance AI workloads
• Develop and maintain data pipelines using Azure Data Factory / Synapse
• Design multi-agent AI orchestration frameworks for enterprise use cases
• Implement vector search solutions using Azure Cognitive Search
• Monitor system performance using Azure Monitor, Log Analytics
• Ensure security, governance, and compliance using Azure IAM and policies

Required Skills & Qualifications

• Strong experience in Python and scripting
• Hands-on expertise in Microsoft Azure cloud platform
• Experience with:
  • Azure Machine Learning
  • Azure Kubernetes Service (AKS)
  • Azure Data Factory
  • Azure Synapse Analytics
• Strong knowledge of Docker & Kubernetes
• Experience with CI/CD tools (Azure DevOps, GitHub Actions)
• Understanding of distributed computing frameworks (Spark, Databricks)
• Experience working with REST APIs and microservices architecture

Generative AI & Advanced Skills (Must-Have)

• Hands-on with:
  • Azure OpenAI Service
  • Prompt engineering and context optimization
• Experience in:
  • LLM fine-tuning (LoRA, QLoRA)
  • Retrieval-Augmented Generation (RAG)
  • Vector databases and embeddings
• Familiarity with frameworks:
  • LangChain / LlamaIndex
• Experience in multi-agent orchestration / agentic AI systems
• Knowledge of model optimization & cost tuning

Preferred Qualifications

• Experience with Azure Databricks
• Knowledge of Infrastructure as Code (Terraform, Bicep)
• Experience with real-time streaming (Event Hub, Kafka)
• Familiarity with security & compliance frameworks (ISO, SOC2, GDPR)
• Exposure to edge AI or hybrid cloud deployments

Nice to Have

• Experience in enterprise AI platforms (banking, insurance, retail)
• Knowledge of data governance and lineage tools
• Experience in high-scale production AI systems

Key Competencies

• Strong system design and architecture skills
• Performance optimization and cost efficiency mindset
• Cross-team collaboration (Data, DevOps, AI teams)
• Problem-solving in distributed environments