Summary
Capgemini is hiring an AI Development Engineer in Dubai to build and integrate enterprise AI solutions. The role covers GenAI applications, RAG pipelines, AI agents, copilots, automation workflows, and reusable platform components. The engineer will develop user interfaces and APIs, work with Azure AI services, and support testing, evaluation, monitoring, performance tuning, and cost optimization. The posting asks for 5–8 years of relevant experience, strong Python and modern frontend skills, and experience delivering AI or enterprise integration solutions.
The posting
As published on capgemini.com. Pulled from the employer's site and shown as they wrote it.
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.