IN_Senior Associate_Azure AI Engg_GCC_Advisory_Bangalore

1. Azure Cloud Architecture & Implementation

• Design and architect scalable, multi-tenant cloud solutions on Microsoft Azure

• Implement microservices and serverless architectures using Azure Functions, App Service, and Container Instances

• Deploy and manage containerised applications using Docker and Azure Kubernetes Service (AKS)

• Design event-driven architectures using Azure Event Hub and Service Bus

2. Azure AI Foundry & Agentic Solutions Development

• Design and develop intelligent agentic systems using Azure AI Foundry / Azure AI Studio

• Build AI-powered agents for autonomous decision-making, workflow orchestration, and healthcare operations automation

• Integrate Azure OpenAI, Azure Cognitive Services, Azure AI Search, and Document Intelligence into production healthcare applications

• Implement multi-agent orchestration patterns — including supervisor, specialist, and swarm agent topologies

• Design and implement RAG (Retrieval-Augmented Generation) pipelines with vector databases for healthcare knowledge retrieval

• Design agent memory, context management, and stateful conversation patterns

• Evaluate and monitor LLM/agent quality using PromptFlow, RAGAS, or LangSmith

3. Cross-Platform Enterprise Integration

• Integrate agentic solutions with Oracle ERP/HCM, Pega decision platforms, and ServiceNow workflows

• Build API gateway patterns that route agent invocations across Oracle, Azure, and ServiceNow systems

• Understand and navigate RPA-to-Agentic migration patterns — redesigning UiPath/RPA workflows as intelligent, autonomous agents

• Support healthcare data integration: FHIR APIs, HL7 message formats, EDI 835/837 claims processing

4. Backend Development with Python

• Develop robust backend services using Python with FastAPI and Flask

• Build RESTful and async APIs on Azure infrastructure

• Implement Azure Cosmos DB, Azure SQL Database, and Azure Table Storage solutions with optimised query patterns

• Write clean, maintainable, and well-documented code adhering to enterprise security and compliance standards

5. Azure DevOps & Infrastructure

• Set up and maintain CI/CD pipelines using Azure DevOps or GitHub Actions

• Implement Infrastructure as Code (IaC) using Terraform or Azure Resource Manager (ARM) / Bicep

• Monitor and optimise cloud infrastructure using Azure Monitor, Application Insights, and Cost Management

• Manage secrets, encryption, and HIPAA-aligned security compliance using Azure Key Vault

6. Collaboration & Delivery

• Work closely with PGC healthcare stakeholders, domain experts, and cross-functional delivery teams

• Mentor junior developers and cross-skilling UiPath engineers on Azure cloud and Agentic AI patterns

• Produce architecture decision records, technical specifications, and solution design documents

• Participate in discovery workshops and ROI modelling for new agentic use cases

Experience Level

• 5–8 years of professional software development and cloud engineering experience

• 4–5 years hands-on experience with Microsoft Azure platform (architecture and production deployments)

• 4 years of production-level Python development

• 3+ years hands-on experience with Azure AI services and LLM/Agentic AI integrations

Core Technical Skills

Python & Frameworks:

• Production-level Python with FastAPI and Flask (async/await, dependency injection, middleware)

• Async programming: asyncio, aiohttp, concurrent.futures

• Packaging, virtual environments, unit testing (pytest), and code quality tooling

Azure Platform Services:

• Azure Compute: Functions, App Service, Container Instances, Virtual Machines, AKS

• Azure Data: Cosmos DB, SQL Database, Azure Storage, Data Lake, Event Hub, Service Bus

• Azure Networking: VNet, Application Gateway, API Management, Private Endpoints

• Azure Identity: Azure AD / Entra ID, RBAC, Managed Identities

• Azure AI: Azure OpenAI, Cognitive Services, Azure AI Search, Document Intelligence, Content Safety

• Azure DevOps: Pipelines, Repos, Artifacts, Board tracking

Azure AI Foundry & Agentic AI:

• Azure AI Studio and AI Foundry platform — model deployment, evaluation, prompt management

• Agentic frameworks: LangChain, AutoGen, CrewAI, Semantic Kernel

• Multi-agent orchestration: supervisor/worker topologies, task routing, agent-to-agent communication

• RAG pipeline design: chunking strategies, embedding models, vector retrieval, re-ranking

• Vector databases: Azure AI Search (vector mode), Azure Cosmos DB (vector), pgvector, Pinecone

• LLM evaluation and monitoring: RAGAS, PromptFlow, LangSmith, Azure AI Evaluations

• Structured output generation, function calling, and tool use patterns

• Agent memory patterns: short-term (context window), long-term (retrieval), episodic (session logs) \

• Container & Orchestration:

  • Docker containerisation: multi-stage builds, slim images, container registries

  • AKS deployment and management: Helm charts, namespaces, autoscaling, pod security

  Infrastructure as Code:

  • Terraform for Azure: state management, modules, workspace strategy

  • Azure Resource Manager (ARM) templates and Bicep

  Database Design & Optimisation:

  • Azure SQL Database: query optimisation, indexing, partitioning

  • NoSQL design with Azure Cosmos DB: partition key strategy, TTL, change feed

  • Vector store design: schema, metadata filtering, hybrid search

  Healthcare & Compliance Context (Preferred):

  • HIPAA compliance awareness in cloud architecture (PHI data handling, audit logging)

  • HL7/FHIR API familiarity for healthcare data exchange

  • EDI 835/837 basic familiarity for claims processing workflows

  • RPA-to-Agentic migration: understanding of process redesign from UiPath/RPA to agentic patterns