The Azure AI Engineer is responsible for the end-to-end implementation and deployment of enterprise AI solutions on the Azure Stack. You will take ownership of building, integrating, and operationalizing AI workloads using Azure AI Foundry, Azure Data Lake, and the broader Microsoft AI ecosystem — including the design and enforcement of guardrails for responsible, secure, and compliant AI.
This is a hands-on engineering role focused on delivery: turning architectural designs into production-ready AI services, owning the deployment lifecycle, and ensuring solutions are robust, observable, and aligned with enterprise security and governance standards
Responsibilities:
AI Solution Implementation
- Solution Build: Implement AI solutions on Azure AI Foundry — including agent design, model selection, prompt flows, evaluation pipelines, and deployment of base and fine-tuned models.
- Generative AI & RAG: Build retrieval-augmented generation (RAG) pipelines using Azure AI Search, Azure OpenAI, and vector stores; consume curated data from upstream data platforms.
- Model Deployment:
Deploy models and AI endpoints to Azure Machine Learning, Azure AI Foundry, and Azure Container Apps; manage endpoint scaling, versioning, and traffic routing.
- Integration: Integrate AI services with downstream applications via REST APIs, Azure API Management, and Function Apps.
AI Guardrails & Responsible AI
- Guardrails Implementation: Implement input/output guardrails using Azure AI Content Safety, Prompt Shields, and groundedness checks; configure jailbreak, PII, and harmful-content filters.
- Evaluation: Build evaluation pipelines for safety, groundedness, relevance, and bias using Azure AI Foundry evaluations; embed Responsible AI checks into the deployment workflow.
- Security Awareness: Work within enterprise security patterns — Managed Identity, Key Vault, private endpoints, and RBAC — for all AI services.
Deployment & MLOps
- CI/CD for AI: Build and maintain CI/CD pipelines (Azur
📌 Azure AI Engineer (Kochi)
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📍 Kochi