- Bachelors Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or a related field with 0-2 years of relevant experience

- Masters/Doctorate/PGDM in a related field.

Technical Skills :

- Proficient in Python with extensive hands-on experience in machine learning, deep learning, and generative AI using frameworks and libraries such as scikit-learn, TensorFlow, PyTorch, Keras, and NLTK, along with strong grounding in statistical modeling, embeddings, text classification, transformers, and large language models (LLMs).

- Experienced in designing agentic AI and advanced RAG systems using libraries such as LangChain and LangGraph, involving multi-step reasoning, tool-using agents, memory management, query planning, and hybrid ML-LLM architectures (e.g., ReAct-style agents, retrieval-augmented workflows).

- Familiarity with deploying and managing AI models on cloud platforms such as AWS, GCP, or Azure.

- Working knowledge of GitHub for code management and deployment.

Desired Skills and Key Responsibilities :

- Strong analytical and problem-solving abilities, with the capacity to design and reason through complex data and AI workflows, derive actionable insights, and debug multi-step ML and agentic AI systems.

- Experience in building and maintaining data pipelines and AI workflows to support ML, LLM, and agentic systems, including the use of language models such as GPT, Qwen or Google Gemini to develop hybrid solutions that combine classical machine learning, retrieval-augmented generation, and autonomous reasoning.

- Creative acumen to conceptualize, design, and implement scalable ML, generative AI, and agentic architectures involving multi-step reasoning, tool integration, and memory-driven workflows.

- Excellent communication and collaboration skills to work effectively with both technical and non-technical stakeholders, including the ability to explain AI-driven decisions, system behavior, and trade-offs clearly.

- Familiarity with the end-to-end AI product lifecycle, from ideation and prototyping to deployment, evaluation, and iterative improvement, particularly in the context of ML, generative AI, and agent-based solutions.

- A strong passion for continuous learning and staying updated with advancements in AI, machine learning, agentic systems, and product development, fostering innovation, ownership, and practical impact.

- Strong ownership and accountability with a proactive, entrepreneurial approach to problem-solving and execution.