Ai ML Engineer

Job Description: Data Scientist – Agentic AI & GenAI Experience: 3–6 years Location: Jaipur, Bangalore, Noida | Employment Type: Full-time About the Role We are looking for a Data Scientist with a strong focus on Agentic AI and Generative AI to design, build, and deploy intelligent systems that go beyond traditional predictive modeling. The ideal candidate combines solid data science fundamentals with hands-on experience building LLM-powered agents, and brings sharp logical reasoning and clear communication to cross-functional collaboration. Key Responsibilities Design and deploy agentic AI systems (multi-step reasoning, tool-use, planning) using frameworks like LangChain, LangGraph, or CrewAI. Build GenAI solutions including RAG pipelines, conversational agents, and content generation using LLMs. Apply prompt engineering and evaluation techniques to improve model accuracy and reliability. Own the ML lifecycle — from data prep to deployment and monitoring. Collaborate with cross-functional teams to translate business problems into AI solutions. Communicate findings and trade-offs clearly to technical and non-technical stakeholders. Required Skills & Qualifications 3–6 years of experience in Data Science / Machine Learning, with at least 1 year focused on GenAI/LLM-based projects. Strong programming skills in Python Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, etc.). Practical experience building agentic workflows — planning, tool-calling, multi agent coordination, memory management. Solid understanding of RAG architectures, vector databases, and embedding models. Experience with prompt engineering, evaluation frameworks (e.g., RAGAS, custom eval harnesses), and LLM fine-tuning (LoRA/PEFT is a plus). Decent foundation in statistics, ML algorithms, and data manipulation (SQL, Pandas). Excellent logical reasoning and problem-solving ability - comfortable breaking down ambiguous, open-ended problems into structured approaches. Good communication skills — able to explain technical concepts to non-technical stakeholders and document work clearly. Good to Have Understanding of productionizing ML and GenAI workloads Familiarity with model safety, guardrails, and responsible AI practices. Working knowledge of cloud platforms (AWS/Azure/GCP) and MLOps practices (model deployment, monitoring, versioning).