Generative AI Engineer

4 weeks ago

Chennai, India Saint-Gobain Full time

Job Title: Data Scientist – RAG & Edge AI SystemsLocation: ChennaiExperience: 3+ yearsJoining: Immediate to 30 days preferredAbout the RoleWe are seeking a Data Scientist with strong experience in RAG (Retrieval-Augmented Generation) , LLMs , and Edge AI processing to join our growing AI engineering team.This role combines hands-on engineering with strategic AI designKey ResponsibilitiesDesign and develop LLM and RAG-based systems that enhance knowledge discovery, automation, and decision support.Build and orchestrate end-to-end AI pipelines using Azure AI Studio , Logic Apps , and Data Factory .Develop APIs and microservices for AI model integration with business and plant systems.Optimize and deploy deep learning models to edge devices (e.g., NVIDIA Jetson , AI PCs ) for real-time inference.Fine-tune LLMs for domain-specific tasks , balancing accuracy, speed, and compute efficiency.Collaborate cross-functionally with data engineers, software developers, and domain experts to bring AI prototypes to production.Monitor model performance, automate retraining pipelines, and ensure compliance with data governance and security standards .Required Skills & ExperienceRAG & LLMs: Experience building Retrieval-Augmented Generation systems using frameworks such as LangChain , LlamaIndex , or Azure OpenAI Service .Azure Stack: Hands-on experience with Azure AI Studio , Azure Machine Learning , Logic Apps , Functions , and Data Factory .Programming: Strong proficiency in Python , with experience in PyTorch , TensorFlow , LangChain , and FastAPI .Edge AI Processing:Deploying and optimizing models on edge devices such as NVIDIA Jetson , Intel AI PCs , or ARM-based systems .Proficiency in TensorRT for real-time inference.Familiarity with Docker and remote monitoring for distributed edge workloads.Data Handling: Experience with SQL and vector databases for retrieval and embedding management.Visualization & Monitoring: Experience building dashboards using Power BI , Streamlit , or similar tools for inference metrics and explainability.Preferred / Nice-to-HaveStrong understanding of vector search optimization , document embeddings , and semantic retrieval at scale.Experience with multimodal AI systems (text and image) for industrial or enterprise use.Understanding of model compression , quantization , and GPU optimization for on-device AI.Experience integrating AI models with manufacturing systems (MES, SCADA, or PLC data).Familiarity with Elixir/Phoenix for chatbot or API integration within enterprise platforms.Awareness of AI safety, ethical AI , and data privacy frameworks in regulated environments.

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