Carrier: Associate, AI & Data Engineering

Job Description

The Associate, AI & Data Engineering role at Carrier in Bengaluru is designed for technology professionals who want to build enterprise-scale capabilities across artificial intelligence, data engineering, cloud platforms, automation, and responsible AI. The position focuses on integrating and operationalizing modern AI platforms such as Microsoft Copilot, Copilot Studio, Google Gemini Enterprise, Google Workspace, and Vertex AI within a secure enterprise environment. Engineers will contribute to solution architecture, custom connectors, enterprise integrations, data ingestion, and Retrieval-Augmented Generation pipelines that help AI applications deliver accurate and context-aware responses. The role provides an opportunity to work at the intersection of generative AI, cloud engineering, enterprise data, and automation while helping Carrier transform business processes through scalable and governed digital solutions.

A key part of the position involves developing agentic AI and automation workflows that connect low-code platforms with full-code engineering solutions. The engineer may work with Semantic Kernel, Azure AI Agent Service, Python, TypeScript, Power Automate, Logic Apps, and other modern technologies to build intelligent workflows and enterprise integrations. The role also requires evaluating emerging AI technologies and platforms, understanding their capabilities, and comparing them against organizational requirements before providing recommendations to technical and business stakeholders. Data grounding is another important responsibility, requiring knowledge of embeddings, vector databases, graph data, enterprise content platforms, and retrieval technologies. These capabilities enable AI systems to work with enterprise information while producing useful, relevant, and context-aware results.

The position also places strong emphasis on AI governance, security, MLOps, LLMOps, observability, and cost optimization. Engineers will help ensure that AI solutions respect user permissions, data boundaries, privacy requirements, regional data residency rules, and enterprise security policies. The role includes exposure to Microsoft Purview, Microsoft Entra ID, OAuth 2.0, Azure AI Foundry, Azure Monitor, Application Insights, GitHub, and CI/CD pipelines. Candidates will contribute to the operational lifecycle of AI models, prompts, agents, and platform configurations while monitoring usage, performance, response quality, API latency, and costs. For professionals with 0–2 years of experience, this opportunity offers a strong foundation for careers in AI platform engineering, data engineering, cloud engineering, generative AI, MLOps, LLMOps, and enterprise automation.

Roles & Responsibilities

1. Design Enterprise AI Platforms
   Contribute to the design, deployment, and maintenance of enterprise AI architectures involving Microsoft Copilot, Copilot Studio, Google Gemini Enterprise, Google Workspace, and Vertex AI.
2. Build Custom AI Integrations
   Develop enterprise connectors, plugins, APIs, and OpenAPI manifests that allow AI platforms to securely interact with proprietary databases, ERP applications, legacy systems, and internal business services.
3. Develop RAG Pipelines
   Design, implement, and optimize Retrieval-Augmented Generation workflows using enterprise data sources and cloud APIs to improve the accuracy, relevance, and contextual understanding of AI-generated responses.
4. Create Agentic AI Workflows
   Build autonomous and multi-agent workflows using technologies such as Semantic Kernel, Azure AI Agent Service, Python-based orchestration, and other emerging agent development frameworks.
5. Integrate Automation Platforms
   Connect Power Platform, Power Automate, and Logic Apps with Python or TypeScript-based backend services to create scalable automation solutions that combine low-code and full-code development.
6. Evaluate Emerging AI Technologies
   Research and assess new AI platforms and tools such as Codex, Claude, Kong AI, and other emerging technologies by comparing their capabilities, enterprise suitability, security considerations, and potential business value.
7. Implement AI Governance
   Support enterprise AI governance by applying security controls, tenant isolation, Data Loss Prevention policies, compliance requirements, and responsible AI practices across organizational environments.
8. Manage Access and Data Security
   Ensure AI applications respect user-level permissions and enterprise data boundaries by working with Microsoft Entra ID, OAuth 2.0, Microsoft 365 security controls, and regional data residency requirements.
9. Monitor AI Performance and Costs
   Track AI platform usage, API performance, response quality, latency, licensing, and cost trends while developing dashboards through tools such as Power BI or Looker.
10. Support Power Platform Administration
    Assist with governance and administration of Microsoft Power Platform and Microsoft 365 environments, including security, DLP, application lifecycle management, compliance, environment management, and release processes.
11. Implement MLOps and LLMOps Practices
    Help operationalize machine learning and generative AI solutions through Azure AI Foundry, Azure Monitor, Application Insights, GitHub, and CI/CD pipelines while supporting model, prompt, and agent lifecycle management.
12. Document and Collaborate
    Clearly document technical solutions, configurations, workflows, and implementation decisions while collaborating with engineering, data, security, product, and business teams to deliver reliable AI capabilities.

Requirements & Eligibility

1. Bachelor’s Degree
   Candidates should hold a bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or another closely related technical discipline.
2. 0–2 Years of Relevant Experience
   The position is suitable for early-career professionals with 0–2 years of experience in AI platforms, cloud engineering, data platforms, automation, enterprise applications, or related technology areas.
3. Programming Knowledge
   Candidates should have basic hands-on programming experience in Python, TypeScript, JavaScript, or a comparable programming language. Strong Python or TypeScript skills are particularly useful for integrations and AI automation.
4. Enterprise AI Platform Exposure
   Familiarity with Microsoft Copilot Studio, Power Platform, Google Gemini Enterprise, Vertex AI, or comparable enterprise AI technologies is valuable for contributing to the organization’s AI platform initiatives.
5. Cloud Platform Understanding
   Applicants should understand fundamental cloud concepts and preferably have exposure to Microsoft Azure, Azure AI Services, Azure AI Foundry, Google Cloud Platform, or similar enterprise cloud ecosystems.
6. Generative AI Fundamentals
   Candidates should understand fundamental concepts involving generative AI, large language models, prompts, APIs, embeddings, retrieval, AI agents, and model lifecycle management.
7. Data Engineering Knowledge
   Basic knowledge of enterprise data integration, graph data, embeddings, vector databases, content repositories, and platforms such as SharePoint, OneDrive, and Google Drive is beneficial.
8. DevOps and CI/CD Awareness
   Familiarity with GitHub, source control, continuous integration, continuous deployment, automated releases, and deployment pipelines will help candidates contribute to AI platform engineering activities.
9. Security and Responsible AI Mindset
   Candidates should understand fundamental concepts involving access control, data privacy, compliance, secure AI implementation, responsible AI, and protection of enterprise information.
10. Communication and Ownership
    Strong communication, documentation, problem-solving, adaptability, and ownership skills are important. Candidates should be comfortable learning new technologies quickly and collaborating with cross-functional teams.

Expected Salary

For an Associate AI & Data Engineering professional with 0–2 years of experience in Bengaluru, a realistic market-oriented salary expectation is approximately ₹5 LPA–₹10 LPA, depending on the candidate’s technical background, AI/cloud exposure, programming skills, and relevant project experience. Entry-level AI and data engineering compensation can vary significantly because specialized generative AI and cloud skills may command a premium over conventional entry-level software roles.

For this Carrier opportunity, the actual compensation will depend on Carrier’s internal salary structure, candidate qualifications, and final job level. Candidates with practical experience in Python, Azure, Google Cloud, generative AI, RAG, vector databases, Power Platform, CI/CD, or AI agents may be positioned toward the stronger end of the applicable market range.

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