Azure AI Engineer

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 (Azure DevOps or GitHub Actions) for prompt flows, model evaluation, and endpoint deployment; implement model registry and promotion gates. Observability: Instrument AI workloads with Azure Monitor, Application Insights, and Log Analytics; set up dashboards for token usage, latency, cost, and guardrail violations. Infrastructure as Code: Contribute to Terraform or Bicep modules for AI Foundry, AML, and AI Search resources (working alongside platform engineering for foundational infra). Requirements Azure AI Stack: Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and Azure AI Content Safety. GenAI Engineering: Experience building RAG, agentic, or prompt-flow solutions; familiarity with frameworks such as LangChain, Semantic Kernel, or LlamaIndex. Programming: Strong Python skills; comfortable with REST APIs, async patterns, and SDK-based integrations (Azure SDK, OpenAI SDK). Data Awareness: Working familiarity with Azure Data Lake Storage Gen2 and Delta tables — sufficient to consume curated data for AI use cases (deep Databricks/PySpark engineering not required). DevOps: Hands-on with Azure DevOps or GitHub Actions for CI/CD; Git-based workflows. Infrastructure as Code: Comfortable contributing to existing Terraform or Bicep modules. Containers & APIs: Working knowledge of Docker and Azure Container Apps for deploying AI services. Observability: Azure Monitor, Application Insights, and Log Analytics for AI workload telemetry. Guardrails: Practical experience implementing content safety, prompt shields, or groundedness checks in production AI systems. Evaluation: Familiarity with offline and online evaluation methods for LLM applications (groundedness, relevance, safety). Responsible AI Awareness: Understanding of Responsible AI principles and regional compliance considerations relevant to UAE / regulated industries. Collaboration: Works closely with architects, data engineers, and security teams; communicates clearly across technical and non-technical audiences. Documentation: Produces clear technical design documents and deployment runbooks. Experience: 3–5 years of hands-on engineering experience, including at least 2 years building AI or ML solutions on Azure. Education: Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience). Certifications (preferred): Microsoft Certified: Azure AI Engineer Associate (AI-102). Location: Remote, aligned to GST business hours. Engagement: Full-time.