AI Engineer – Azure OpenAI & Retrieval Systems(RAG)
TAT IT TechnolgiesUnited Arab Emirates12 hours ago
ContractRemote FriendlyEngineering, Information Technology
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We have an urgent requirement for AI Engineer – Azure OpenAI & Retrieval Systems(RAG) is required for our banking client in Abu Dhabi, UAE
Strong experience designing and building RAG(Retrieval-Augmented Generation) systems end-to-end is THE MUST
Strong Experience On Azure OpenAI Service Is THE MUST
Strong experience on Azure AI Search (Vector + Semantic Search) is THE MUST
Experience on End-to-End AI Application Engineering (Python + APIs) is MUST
Strong exprince on Developing LLM-powered apps/APIs and deployed them to production is MUST
AI Engineer to design, build, and deploy end-to-end AI solutions using Microsoft Azure AI services, with a strong emphasis on Azure OpenAI, Azure AI Search, and Retrieval-Augmented Generation (RAG) architectures. This role is fully hands-on and requires deep expertise across the full AI application lifecycle, from data ingestion to production-grade LLM-powered systems.
Design, develop, and deploy end-to-end AI solutions using Azure OpenAI, Azure AI Search, and RAG architectures
Build and maintain Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, vector indexing, and retrieval strategies
Develop scalable and secure data ingestion pipelines for structured and unstructured data from multiple sources
Implement semantic search solutions using Azure AI Search, vector search, and hybrid search approaches
Design and manage prompt orchestration frameworks, including prompt templates, versioning, chaining, and evaluation
Build LLM-powered applications (chatbots, copilots, assistants, APIs) with strong grounding, relevance, and factual accuracy
Ensure performance optimization, low latency, and cost efficiency for AI workloads
Apply security best practices, including data privacy, access controls, encryption, and responsible AI principles
Implement techniques to reduce hallucinations and improve model grounding using retrieved enterprise data
Monitor, evaluate, and continuously improve model responses using metrics, logging, and feedback loops
Collaborate with product, data, and cloud engineering teams to integrate AI solutions into existing systems
Deploy AI solutions to production using Azure-native services, following DevOps and MLOps best practices
Mandatory Technical Skills & Experience
Strong experience designing and building RAG(Retrieval-Augmented Generation) systems end-to-end is THE MUST
Strong Experience On Azure OpenAI Service Is THE MUST
Strong experience on Azure AI Search (Vector + Semantic Search) is THE MUST
Experience on End-to-End AI Application Engineering (Python + APIs) is MUST
Strong exprince on Developing LLM-powered apps/APIs and deployed them to production is MUST
AI Engineer to design, build, and deploy end-to-end AI solutions using Microsoft Azure AI services, with a strong emphasis on Azure OpenAI, Azure AI Search, and Retrieval-Augmented Generation (RAG) architectures. This role is fully hands-on and requires deep expertise across the full AI application lifecycle, from data ingestion to production-grade LLM-powered systems.
Design, develop, and deploy end-to-end AI solutions using Azure OpenAI, Azure AI Search, and RAG architectures
Build and maintain Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, vector indexing, and retrieval strategies
Develop scalable and secure data ingestion pipelines for structured and unstructured data from multiple sources
Implement semantic search solutions using Azure AI Search, vector search, and hybrid search approaches
Design and manage prompt orchestration frameworks, including prompt templates, versioning, chaining, and evaluation
Build LLM-powered applications (chatbots, copilots, assistants, APIs) with strong grounding, relevance, and factual accuracy
Ensure performance optimization, low latency, and cost efficiency for AI workloads
Apply security best practices, including data privacy, access controls, encryption, and responsible AI principles
Implement techniques to reduce hallucinations and improve model grounding using retrieved enterprise data
Monitor, evaluate, and continuously improve model responses using metrics, logging, and feedback loops
Collaborate with product, data, and cloud engineering teams to integrate AI solutions into existing systems
Deploy AI solutions to production using Azure-native services, following DevOps and MLOps best practices
Mandatory Technical Skills & Experience
- Strong hands-on experience with Azure OpenAI Service
- Proven experience implementing Retrieval-Augmented Generation (RAG) in production environments
- Expertise with Azure AI Search, including vector search and semantic ranking
- Experience with embeddings, chunking strategies, and vector databases
- Strong proficiency in Python (mandatory)
- Experience building REST APIs and backend services for AI applications
- Knowledge of prompt engineering, prompt chaining, and LLM orchestration frameworks
- Experience with Azure cloud services (App Services, Functions, Storage, Key Vault, Identity, etc.)
- Understanding of LLM limitations, grounding techniques, and hallucination mitigation
- Familiarity with CI/CD, DevOps, and MLOps practices in Azure environments
Key Skills
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