Data Scientist- Generative AI Specialist

2 weeks ago

Noida, Uttar Pradesh, India VLaunchU Full time

Job Title: Data Scientist – Generative AI Specialist

Location: Noida, India

Job Type: Full-Time

Experience Required: 3+ years in Data Science with a strong focus on Generative AI

Budget: Up to 22 LPA

Notice Period: Immediate

About the Role

We are looking for an experienced Data Scientist – Generative AI Specialist to design, develop, and optimize cutting-edge AI solutions. The ideal candidate will have hands-on expertise in Generative AI, Agentic AI, LLMs, and advanced NLP, along with experience deploying AI models at scale in enterprise environments.

Key Responsibilities

  • Build, fine-tune, and deploy Generative AI and LLM-based solutions.
  • Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, and semantic search.
  • Develop and manage Knowledge Graphs to enhance reasoning and context.
  • Apply Prompt Engineering best practices to improve AI output quality.
  • Work with Azure AI ecosystem – Azure OpenAI, Cognitive Services, Azure ML.
  • Leverage LangChain and other Agentic AI orchestration frameworks for intelligent automation.
  • Utilize Hugging Face for model training, customization, and deployment.
  • Integrate and optimize AI models using FastAPI, Databricks, and AWS services.
  • Manage MLOps pipelines using CI/CD, MLflow, and DevOps tools.
  • Work with vector databases (Faiss, Redis) for storage and retrieval of embeddings.
  • Troubleshoot, debug, and improve AI systems for performance and reliability.

Required Skills & Experience

  • 3+ years of Data Science experience, with a focus on Generative AI, Agentic AI, LLMs, and NLP.
  • Strong Python programming skills for AI application development.
  • Expertise in RAG, Knowledge Graphs, Prompt Engineering.
  • Proficiency with Azure OpenAI, Cognitive Services, Azure ML.
  • Hands-on experience with LangChain, Hugging Face, and vector DBs (Faiss, Redis).
  • Familiarity with FastAPI, Databricks, AWS, MLflow.
  • Solid background in MLOps, CI/CD pipelines, and AI deployment optimization.

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