Senior Data Scientist (AI/LLM ) (Chennai)

AI & LLM Engineering - Design and develop LLM-powered features including RAG (Retrieval-Augmented Generation) pipelines, prompt engineering. - Build and evaluate agentic AI systems using frameworks. - Integrate vector databases for semantic search and knowledge retrieval. - Explore self-hosted LLM deployments using Ollama, vLLM, or similar frameworks. Machine Learning & Modeling - Build, train, and evaluate ML models for credit risk, fraud detection, anomaly detection, and customer segmentation. - Perform feature engineering, hyperparameter tuning, model selection, and error analysis. - Apply ensemble methods (boosting, bagging, stacking) to improve model robustness. - Conduct A/B testing, multivariate experiments, and statistical analysis to validate model performance. Cloud & Production - Build and maintain scalable ML pipelines and data workflows on cloud infrastructure. - Collaborate with data engineers to integrate models into production systems using MLOps best practices. - Write clean, production-level Python code and contribute to shared AI tooling and libraries. Who you need to be Minimum Qualification & Experience - 3+ years of industry or project experience in AI/ML engineering (internships and academic projects strongly count). - Bachelors OR masters degree in statistics, computer science, Engineering, Mathematics, or a related technical field. AI & LLM Skills (Must Have) - Hands-on experience or strong project exposure to LLMs prompt engineering, RAG pipelines. - Familiarity with Hugging Face Transformers, OpenAI API, or equivalent LLM frameworks. - Understanding of vector embeddings, semantic search, and knowledge retrieval concepts. - Awareness of GenAI and Agentic AI methodologies and their practical applications. Programming / Cloud / Data Skills (Must Have) - Strong Python programming skills clean, maintainable, production-ready code. - Proficient in ML libraries: scikit-learn, TensorFlow or PyTorch, XGBoost, pandas, NumPy. - Solid SQL skills for data querying, transformation, and mining structured datasets. - Experience normalizing and preprocessing data for consistency, quality, and model readiness. - Working knowledge of at least one major cloud platform: AWS, GCP, or Azure. - Understanding of cloud storage, compute, and containerization basics (Docker, Kubernetes). - Exposure to big data tools such as Spark or Hadoop (MapReduce, Hive, Pig) is a plus. Machine Learning Algorithms (Good to Have) - Clear understanding, coding, implementation, error analysis, and model tuning across: - Supervised Learning: Linear Regression, Logistic Regression, SVM, Decision Trees, Random Forest, XGBoost. - Neural Networks: Shallow Neural Networks and familiarity with deep learning architectures. - Unsupervised Learning: Clustering (K-Means, DBSCAN), Recommender Systems. - Time Series & Anomaly Detection: ARIMA, Isolation Forest, statistical anomaly methods. - Strong command of model selection, cross-validation, feature selection, and ensemble methods (boosting, bagging, stacking). - Ability to perform hyperparameter tuning using Grid Search, Random Search, or Bayesian optimization. Nice to Have - Experience in fintech, credit scoring, risk analytics, or financial inclusion domains. - Contributions to open-source ML/AI projects or a strong personal project portfolio on GitHub. - Familiarity with MCP (Model Context Protocol) or building AI tool integrations. - Experience with MLflow, Weights & Biases, or other experiment tracking tools. Soft Skills - Genuine curiosity about AI/ML and eagerness to learn in a fast-moving field. - Robust problem-solving mindset able to break down complex challenges into actionable steps. - Clear communication skills to present model results and insights to non-technical stakeholders. - Collaborative team player who thrives in a cross-functional, mission-driven environment. Job location - Chennai/Bangalore (work from office-Hybrid) .