2 - 4 years

8 - 13 Lacs

Posted:2 months ago| Platform: Naukri logo

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Job Description

Job Title: Junior Data Science Engineer AI/ML Experience Level: 1 to 2 years About the Role: We're looking for sharp, curious, and motivated junior data science engineers to join our innovation-driven team. In this role, you'll work alongside experienced engineers, data scientists, and product teams to prototype cutting-edge solutions involving AI agents, large language models (LLMs), and unsupervised learning techniques. You'll be exposed to end-to-end solution building from hypothesis to prototype to pilot in a collaborative, fast-paced environment where ideas move quickly from whiteboard to working system. What You’ll Do: Prototype AI agents using LLMs (e.g., OpenAI, Claude, Mistral, etc.) Explore and apply clustering and unsupervised learning methods (e.g., k-means, DBSCAN, topic modeling, dimensionality reduction) Develop proof-of-concept solutions and experiment with ideas across different domains Collaborate with cross-functional teams including ML engineers, product managers, and senior data scientists Analyze structured and unstructured data from various sources (APIs, databases, logs, documents, etc.) Continuously learn, test, and apply new advancements in the AI/ML space What We’re Looking For: Strong foundation in statistics, probability, and machine learning Solid understanding of unsupervised learning algorithms and their use cases Hands-on experience with clustering, anomaly detection, or dimensionality reduction Basic familiarity with LLMs and prompt engineering Proficiency in Python and libraries such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow Familiarity with building prototypes using Jupyter, Streamlit, or similar Growth mindset and a passion for experimentation and rapid learning Nice to Have: Exposure to AI agent frameworks (LangChain, AutoGen, CrewAI, etc.) Experience with API design or integration Familiarity with cloud platforms (AWS/GCP/Azure) Background in NLP, graph analysis, or time-series clustering

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