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Known global brand, mid-level popular GenAI role, and metro/hybrid location drive high competition.
GenAI, LLM, and MLOps skills are transferable across industries but require specialized ML experience.
Explicit 6–8 years requirement plus mandatory GenAI, LangChain, Python, PySpark, and SQL skills raises screening strictness.
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Develop and deploy Generative AI applications using LLMs like GPT, Gemini, and LLaMA and AI frameworks such as LangChain and FastAPI.
Build and manage multi-agent AI systems and design Retrieval-Augmented Generation (RAG) pipelines using vector databases (FAISS, Qdrant, Weaviate, ChromaDB).
Collaborate globally with Analytics teams and stakeholders to deliver AI-driven business solutions and provide insights and recommendations aligned with business needs.
Bachelor's degree in Computer Science, Engineering, Statistics, or a related technical field.
6 to 8 years of experience in Data Analytics, Data Science, or Generative AI roles.
Proficiency in Python, PySpark, SQL, and data science libraries including Pandas, NumPy, Scikit-learn, and Matplotlib.
Experience with GenAI platforms/tools such as OpenAI, Anthropic, Google Vertex AI, and frameworks like FastAPI, Gradio, Streamlit.
Experienced in delivering and deploying production-grade AI and data-driven solutions with strong operational focus.
Strong expertise in Generative AI models and architectures including RAG and Agentic AI using LangChain and Lang Graph.
Able to manage complex projects involving cross-functional and global stakeholders with strong analytical and communication skills.