Associate, AI & Data Engineering at Carrier | Bangalore

About Carrier

Carrier Global Corporation, a global leader in intelligent climate and energy solutions, is dedicated to creating innovations that enhance comfort, safety, and sustainability. Through cutting-edge advancements in climate solutions such as temperature control, air quality, and transportation, Carrier improves lives, empowers critical industries, and ensures the safe transport of food, life-saving medicines, and more. Since inventing modern air conditioning in 1902, Carrier leads with purpose: enhancing the lives we live and the world we share. This leadership is sustained by a world-class, inclusive workforce that prioritizes the customer in every action.

About the Role

This role involves building enterprise data and AI capabilities to enable secure, scalable, and high-quality data-driven decisions. It applies AI/ML, automation, and strong governance to drive efficiency and business value.

Job Responsibilities:

1. Platform Engineering & Architecture
   
   
   • Cross-platform orchestration: Design, deploy, and maintain solution architecture for Microsoft Copilot, Copilot Studio, Google Gemini Enterprise, Google Workspace, and Vertex AI.
   • Custom extensibility: Build and maintain enterprise connectors, plugins, and OpenAPI manifests to integrate AI platforms with proprietary databases, ERPs, and legacy systems.
   • Data grounding and retrieval: Design, tune, and scale Retrieval-Augmented Generation pipelines using Microsoft Graph and Google Cloud APIs to deliver accurate, context-aware AI interactions.
2. Automation & Agentic AI
   
   
   • Multi-agent workflows: Design and implement autonomous workflows using frameworks such as Semantic Kernel, Azure AI Agent Service, or custom Python-based orchestration layers.
   • Low-code and full-code integration: Connect low-code automations across Power Platform, Power Automate, and Logic Apps with programmatic backend scripts in Python or TypeScript.
3. AI Platform Evaluation & Assessment
   
   
   • Emerging AI platform evaluations: Demonstrate flexibility to assess upcoming AI platforms and tools such as Codex, Claude, and Kong AI by conducting structured evaluations, comparing capabilities against enterprise requirements, and producing clear recommendation reports for leadership and stakeholders.
4. Governance, Security & Performance
   
   
   • Enterprise guardrails: Enforce AI governance, tenant isolation, and Data Loss Prevention policies across Microsoft Purview and Google Workspace administration.
   • Access control: Ensure AI outputs respect enterprise data boundaries, user-level permissions, Microsoft Entra ID, OAuth 2.0, and regional data residency requirements.
   • Optimization and observability: Track AI usage, API latency, response quality, and cost trends while building dashboards in Power BI or Looker to optimize licensing and total cost of ownership.
5. Power Platform Administration
   
   
6. Responsible for governing Microsoft Power Platform and M365 environments, including security, DLP policies, ALM, and compliance using Microsoft Purview, with hands-on experience using GitHub and CI/CD pipelines to deploy agents and solutions to production. Ensures scalable, secure, and well-governed automation through standardized environment and release management.
7. MLOps & LLMOps
   
   
8. Responsible for operationalizing ML and generative AI solutions across the Azure AI ecosystem, including Azure AI Foundry, Azure Monitor, and Application Insights, with hands-on experience using GitHub for CI/CD-driven deployments. Ensures reliable, compliant, and cost-efficient AI operations through model and prompt lifecycle management, observability, and responsible AI practices.

Role Purpose:

We are seeking a highly skilled AI Platforms Engineer to drive the integration, scalability, governance, and optimization of enterprise generative AI capabilities across Microsoft Copilot and Google Gemini Enterprise. This role goes beyond tool adoption. The engineer will design secure platform integrations, build custom connectors, enable enterprise data grounding, manage multi-agent workflows, and ensure AI solutions are context-aware, compliant, and aligned with organizational security boundaries.