Associate , AI & Data Engineering

• 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.
• 3. 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.
• 4. Power Platform Administration
  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.
  5. MLOps & LLMOps
  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.
  Required Technical Qualifications
  • Enterprise AI tooling: 0-2 years of hands-on technical experience with Microsoft Copilot Studio Power Platform Google Gemini Enterprise and Vertex AI.
  • Core engineering: Strong proficiency in Python or TypeScript for building custom plugins data ingestion scripts and integrations with LLM APIs.
  • Cloud architecture: Solid understanding of Azure AI Foundry Azure AI Services and Google Cloud Platform.
  • Data systems: Experience with graph data embeddings vector databases and enterprise content management platforms such as SharePoint OneDrive and Google Drive.
  • DevOps and CI/CD: Experience establishing continuous integration and deployment pipelines for AI agents prompt configurations and platform automation.

• 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 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.

• Education: Bachelors degree in Computer Science Data Science Information Systems Engineering or a related field.
• Experience: 0-2 years of relevant experience in AI platforms cloud engineering automation data platforms or enterprise application development.
• Programming foundation: Basic hands-on experience with Python TypeScript JavaScript or a similar programming language.
• Cloud and platform understanding: Familiarity with Microsoft Azure Google Cloud Platform Microsoft 365 Power Platform or comparable enterprise platforms.
• AI and data fundamentals: Basic understanding of generative AI concepts APIs data integration retrieval prompts embeddings or model lifecycle concepts.
• Security mindset: Awareness of access control data privacy compliance and responsible AI principles in enterprise environments.
• Communication and ownership: Ability to learn quickly document solutions clearly collaborate with cross-functional teams and take ownership of assigned tasks.

Carrier is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin disability or veteran status age or any other federally protected class.

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