C
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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