Note: The job is a remote job and is open to candidates in USA. Capgemini is a global leader in engineering services, seeking an AI Engineer to design and implement enterprise solutions leveraging Generative AI and Machine Learning. The role involves developing scalable AI components, integrating them with enterprise applications, and ensuring their secure operation in production environments.
Responsibilities
- Design and implement applications based on Generative AI and Large Language Models (LLM)
- Build enterprise copilots, virtual assistants, and intelligent agents
- Develop semantic search and Retrieval-Augmented Generation (RAG) solutions
- Implement reasoning, orchestration, and automation mechanisms using AI agents
- Integrate AI services with enterprise applications, APIs, and data platforms
- Design data ingestion, transformation, and enrichment pipelines
- Implement knowledge indexing and vectorization strategies. Manage structured and unstructured knowledge repositories
- Ensure the quality, consistency, and traceability of data used by AI models
- Deploy solutions using Azure managed services
- Implement LLMOps, MLOps, and DevSecOps best practices
- Monitor the performance, costs, accuracy, and adoption of solutions
- Manage versions of AI prompts, models, and components
- Apply Responsible AI principles
- Implement security and data protection controls
- Ensure compliance with business and regulatory standards
- Participate in architecture and technical quality reviews
Skills
- Design and implement applications based on Generative AI and Large Language Models (LLM)
- Build enterprise copilots, virtual assistants, and intelligent agents
- Develop semantic search and Retrieval-Augmented Generation (RAG) solutions
- Implement reasoning, orchestration, and automation mechanisms using AI agents
- Integrate AI services with enterprise applications, APIs, and data platforms
- Design data ingestion, transformation, and enrichment pipelines
- Implement knowledge indexing and vectorization strategies. Manage structured and unstructured knowledge repositories
- Ensure the quality, consistency, and traceability of data used by AI models
- Deploy solutions using Azure managed services
- Implement LLMOps, MLOps, and DevSecOps best practices
- Monitor the performance, costs, accuracy, and adoption of solutions
- Manage versions of AI prompts, models, and components
- Apply Responsible AI principles
- Implement security and data protection controls
- Ensure compliance with business and regulatory standards
- Participate in architecture and technical quality reviews
- Azure OpenAI Service
- Azure AI Foundry
- Azure AI Search
- Azure AI Content Safety
- Azure Machine Learning
- Azure AI Document Intelligence
- Azure AI Language Services
- Azure Cognitive Services
- Python
- C#
- .NET 8
- REST APIs
- FastAPI
- ASP.NET Core
- JavaScript/TypeScript
- Semantic Kernel
- LangChain
- LangGraph
- AutoGen
- CrewAI
- OpenAI SDK
- Azure AI SDK
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