Machine Learning & Generative AI Engineer

Role: Experience Level: 10+ Years

location: QATAR

Note :communication (Arabic & English). Gulf nationals only.

Machine Learning & Generative AI Engineer (Mid-Level)

ML | DL | Python | LangChain/LangGraph | Azure AI Foundry | RAG | Vector Stores | Semantic Kernel

Role Summary

Build and deploy enterprise-grade Generative AI solutions with strong grounding and

measurable quality. You will implement RAG pipelines, agentic workflows

(LangChain/LangGraph/ Semantic Kernel), and evaluation/monitoring on Azure AI Foundry

and Azure services, or GCP.

Key Responsibilities

• Implement end-to-end RAG: ingestion, chunking, embeddings, retrieval, reranking,
  
  

and citation/grounding.

• Develop agentic workflows using Semantic Kernel, LangChain and LangGraph
  
  

(tools, routing, memory) with guardrails and safe tool use.

• Integrate vector stores/search (Azure AI Search vector, Pinecone, Weaviate, Milvus,
  
  

pgvector, or equivalent) and maintain reliable data pipelines.

• Build Python services/APIs (FastAPI preferred) with streaming, auth (Entra ID/OIDC
  
  

as applicable), and production-ready error handling.

• Own quality and operations: golden datasets, automated regression evaluation,
  
  

tracing/observability, and latency/cost optimization (caching, batching).

Required Qualifications

• 3-6 years software engineering experience; 1-3 years delivering ML/GenAI features
  
  

to production.

• Strong Python, API development, unit/integration testing, and Git-based workflows
  
  

(Azure DevOps or GitHub).

• Hands-on experience with RAG patterns, embeddings, retrieval strategies, prompt
  
  

design, and LLMs evaluation basics.

• Experience with Azure AI Foundry / Azure OpenAI and deploying on Azure
  
  

(Container Apps, Functions, App Service,

• or AKS).
• Comfortable working with data and documents; strong communication and
  
  

documentation skills.

Great Advantage

• LLMOps: prompt/version governance, tracing (LangSmith/OpenTelemetry), and
  
  

evaluation frameworks.

• Hybrid search and reranking, structured outputs (JSON schema), and prompt-
  
  

injection/tool-safety mitigations.

• Performance testing and cost optimization experience for LLM-based systems.
• Bilingual communication (Arabic & English).
  
  

Typical Tech Stack

• Python (FastAPI), LangChain, LangGraph, Azure AI Foundry, Azure OpenAI, Azure AI
  
  

Search (vector), Semantic Kernel, PyTroch, Keras, Tensorflow.

• Pinecone/Weaviate /pgvector / Azure AI Search, Docker, Azure DevOps, Key Vault,
  
  

Application Insights.

Skills: vector,python,rag,kernel,ml,azure