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AI Engineer - Azure AI Foundry

Exclusive opportunity

Hybrid

AI Engineer - Azure AI Foundry

CGI

AI Engineer - Azure AI Foundry

Skills

PythonMicroservicesAIGitHubComplianceBusiness TransformationAzure

25 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

30 September 2026

What they need


We are seeking for a 5+ years of experienced AI Engineer to design, develop, and deploy enterprise-grade AI solutions using Azure AI Foundry and the broader Microsoft AI ecosystem.

The role will focus on building Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), and intelligent automation solutions that accelerate business value while ensuring security, governance, scalability, and responsible AI compliance.

The successful candidate will collaborate with business stakeholders, architects, data engineers, and product teams to transform business challenges into production-ready AI applications and intelligent agents.

Roles & Responsibilities:

1. Azure AI Solution Architecture & Agent Development:

  • Azure AI Foundry Implementation: Design, construct, and manage enterprise Generative AI systems and autonomous agentic workflows using Azure AI Foundry and Azure OpenAI Service.
  • Enterprise RAG Systems: Implement dense retrieval and hybrid search architectures using Azure AI Search, cognitive search capabilities, and vector stores.
  • Responsible AI & Safety: Embed Responsible AI standards, content filtering, and safety guardrails across all Azure OpenAI deployments.

2. Microservices & Serverless Integration:

  • API & Microservice Engineering: Develop clean, scalable Python services and REST APIs to serve AI models within microservice architectures.
  • Cloud Integration & Orchestration: Connect AI agent components with Azure serverless compute using Azure Functions, Logic Apps, Azure Container Apps, and Azure API Management (APIM).

3. MLOps, CI/CD & Performance Optimization:

  • Automated CI/CD: Establish deployment automation for AI assets, prompts, and code using Git, Azure DevOps / GitHub Actions, and Infrastructure as Code.
  • Monitoring & Evaluation: Continually evaluate model accuracy, context relevance, latency, and token consumption in live environments.

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