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AI Development FDE

Exclusive opportunity

Hybrid

AI Development FDE

Capgemini

AI Development FDE

Skills

NextReactTypescriptPythonHTMLTailwind CSSGitMachine LearningAINo-code AutomationsFastAPIAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Dubai - United Arab Emirates

Work mode:

Hybrid

Published on:

12 August 2026

What they need


My Role

  • Develop and integrate enterprise AI solutions, including GenAI apps, RAG pipelines, AI agents, copilots, automation workflows and reusable AI platform components.
  • Build GenAI apps, RAG components, prompt flows, data connectors, APIs and AI agents.
  • Design and develop responsive user interfaces using React, Next.js, TypeScript, HTML5, CSS3, and modern UI component libraries (Material UI, Tailwind CSS or similar).
  • Implement vector indexes, embeddings, document processing and knowledge grounding using Azure AI services.
  • Create PoCs, MVPs and reusable accelerators for client demonstrations and delivery mobilization.
  • Support testing, evaluation, groundedness checks, performance tuning, monitoring and cost optimization.
  • Document designs, deployment steps and reusable implementation patterns.
  • Collaborate with consultants, product owners, architects, UX designers and governance specialists.

Experience And Skills Required

  • 5-8 years in software, data, AI, cloud development or automation.
  • Hands-on work building GenAI, Agentic Harness, RAG, chatbot, AI/ML or enterprise integration solutions.
  • Good Python and frontend capability plus APIs, SDKs and source control.
  • Experience in agile teams and ability to communicate technical trade-offs clearly.
  • Python, FastAPI or similar backend and frontend frameworks.
  • Strong experience in Python and modern frontend development using React or Next.js.
  • Azure OpenAI, Azure AI Foundry, Azure AI Search and Azure ML fundamentals.
  • RAG pipelines, embeddings, vector search, prompt engineering and evaluation.
  • Agent frameworks, orchestration, tool calling and workflow automation.
  • APIs, databases, message queues, files and enterprise connectors.
  • Git, CI/CD, containers, configuration and automated testing.
  • AI quality: test datasets, regression checks, hallucination checks, groundedness, latency and cost.
  • Responsible AI implementation: content filters, guardrails, logging and human review.

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