Azure+AIFoundry - Agentic MLOPS

Capgemini; Bengaluru; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

Capgemini seeks an AI Engineer to design, develop and deploy Generative AI applications and agents using Microsoft Foundry, leveraging its SDKs and enterprise frameworks. You will evaluate foundation models and build grounding, retrieval, and tool- integration workflows across enterprise data sources and APIs.

The role requires 5+ years delivering AI solutions in enterprise environments, with experience in CI/CD, security, tracing and production support, and a strong focus on reliability and

Qualifications

• 5+ years of professional experience in software, cloud, data or AI engineering, including hands-on delivery of enterprise Generative AI solutions.
• Experience designing, developing and deploying AI applications using Microsoft Foundry or comparable Azure-based AI services.
• Experience in enterprise grounding and retrieval, AI agents and tool integration, or model-powered applications.
• Experience integrating AI solutions with enterprise data sources, APIs, Azure services and business applications.
• Experience implementing software engineering practices including source control, automated testing, CI/CD and production support.

Responsibilities

• Design, develop and deploy Generative AI applications and AI agents using Microsoft Foundry, its SDKs and supported agent development frameworks.
• Evaluate and select appropriate foundation models based on business requirements, quality, latency, cost, security and regional availability.
• Design grounding and retrieval solutions that connect AI applications to approved enterprise knowledge and data sources.
• Build agentic workflows that use tools, APIs and enterprise systems to retrieve information and execute business actions.
• Implement prompt, context and tool orchestration patterns that produce reliable, explainable and maintainable agent behavior.
• Develop automated evaluations for response quality, groundedness, relevance, task completion, tool-call accuracy and content safety.
• Establish release criteria and quality gates for AI applications before deployment to production.
• Implement tracing, logging, monitoring and operational dashboards to assess application quality, latency, token consumption, failures and safety signals.
• Integrate Foundry solutions securely with Azure services, Microsoft 365, Power Platform and other enterprise applications.
• Apply Microsoft Entra ID, Azure role-based access control, managed identities, secrets management and least-privilege principles.
• Package and deploy AI applications using source control, automated testing and CI/CD practices.
• Troubleshoot and optimize AI solutions for performance, scalability, reliability, maintainability and cost.
• Produce technical designs, evaluation reports, operational documentation and reusable engineering components.
• Support UAT, production deployment, incident resolution and continuous improvement of deployed AI solutions.
• Collaborate with Solution Designers, Business Analysts, Security, Architecture and Platform teams throughout the solution lifecycle.
• Monitor developments in Microsoft Foundry, foundation models and agent engineering, and assess their relevance for enterprise adoption.

Skills

Generative AI

AI engineering

Cloud services

CI/CD

Security

Education

Bachelors degree in Computer Science or Information Technology

Tools

Microsoft Foundry

Azure services

APIs

Job description

Role & responsibilities

AI Engineer Microsoft Foundry

Key Responsibilities:

• Design, develop and deploy Generative AI applications and AI agents using Microsoft Foundry, its SDKs and supported agent development frameworks.
• Evaluate and select appropriate foundation models based on business requirements, quality, latency, cost, security and regional availability.
• Design grounding and retrieval solutions that connect AI applications to approved enterprise knowledge and data sources.
• Build agentic workflows that use tools, APIs and enterprise systems to retrieve information and execute business actions.
• Implement prompt, context and tool orchestration patterns that produce reliable, explainable and maintainable agent behavior.
• Develop automated evaluations for response quality, groundedness, relevance, task completion, tool-call accuracy and content safety.
• Establish release criteria and quality gates for AI applications before deployment to production.
• Implement tracing, logging, monitoring and operational dashboards to assess application quality, latency, token consumption, failures and safety signals.
• Integrate Foundry solutions securely with Azure services, Microsoft 365, Power Platform and other enterprise applications.
• Apply Microsoft Entra ID, Azure role-based access control, managed identities, secrets management and least-privilege principles.
• Package and deploy AI applications using source control, automated testing and CI/CD practices.
• Troubleshoot and optimize AI solutions for performance, scalability, reliability, maintainability and cost.
• Produce technical designs, evaluation reports, operational documentation and reusable engineering components.
• Support UAT, production deployment, incident resolution and continuous improvement of deployed AI solutions.
• Collaborate with Solution Designers, Business Analysts, Security, Architecture and Platform teams throughout the solution lifecycle.
• Monitor developments in Microsoft Foundry, foundation models and agent engineering, and assess their relevance for enterprise adoption.

Role Specifications:

**

Academic / Professional Qualifications

• Bachelors degree or equivalent professional degree in computer science of Information Technology

Work Experience

• Minimum 5 years of professional experience in software, cloud, data or AI engineering, including hands-on delivery of enterprise Generative AI solutions.
• Demonstrated experience designing, developing and deploying AI applications using Microsoft Foundry or comparable Azure-based AI services.
• Proven delivery experience in one or more of the following areas: enterprise grounding and retrieval, AI agents and tool integration, or model-powered applications.
• Experience integrating AI solutions with enterprise data sources, APIs, Azure services and business applications.
• Experience in applying software engineering practices including source control, automated testing, CI/CD and production support.
• Experience implementing operational controls for AI solutions, including evaluation, telemetry, tracing or monitoring.