Sr Lead Architect

Area(s) of responsibility

• Architect end-to-end GenAI-first and Agentic AI solutions on Azure using:
• Azure OpenAI Service
• Azure AI Foundry
• Azure Agents (Agent Service + Agent Runtime)
• Microsoft Agent Framework SDK
• Azure Machine Learning (Azure ML) for model lifecycle, fine-tuning & evaluation
• Design multi-agent systems capable of planning, reasoning, memory management, tool invocation, and complex workflow execution.
• Architect enterprise-grade RAG systems using:
• Azure Cognitive Search
• Azure AI Search Vector Stores
• Cosmos DB + pgvector
• Redis Enterprise on Azure
• Build secure prompt pipelines, embeddings workflows, tool integration layers, and model evaluation frameworks optimized for Azure’s GenAI stack.

• Build Python-based microservices (FastAPI/Django) tightly integrated with Azure AI and cloud-native components.
• Implement enterprise-grade LLMOps using:
• Azure ML Pipelines
• Azure DevOps (ADO)
• GitHub Actions for MLOps/LLMOps
• Azure Monitor, App Insights, Log Analytics for observability
• Establish engineering standards for:
• Agent governance & safety
• Content filters & guardrails (Azure OpenAI Safety Systems)
• Secure model invocation via private endpoints
• Latency reduction, performance tuning & cost governance
• Optimize AI workloads using model caching, selective batching, adaptive routing, vector index optimization, and autoscaling strategies on AKS/Functions.
• Lead architecture reviews, threat modeling, cost modeling, and performance benchmarking for large-scale GenAI workloads.

• Evaluate new Azure GenAI capabilities, FMs, agent frameworks, vector databases, and orchestration tools.
• Build internal accelerators and reusable reference architectures for:
• Multi-agent orchestration with Azure Agents Framework, Langgraph, CrewAI
• RAG 2.0 and context enrichment pipelines
• Federated & hybrid retrieval patterns
• Enterprise system/tool integration
• Drive PoCs, prototypes, and tech spikes to validate emerging Azure GenAI capabilities and accelerate enterprise adoption.

• Microsoft Certified Azure Solutions Architect Expert or equivalent Azure Architect certification.
• Strong hands-on experience designing AI/ML or GenAI systems on Azure.