Data Scientist AI/ML & Agentic Systems - by 15th April, 2026

Location: Kolkata (On-site) Experience: 4-5 Years Education: Masters degree in computer science /engineering/ data science Employment Type: Full-Time Role Overview We are seeking a highly skilled Data Scientist with expertise in Large Language Models (LLM), Machine Learning, Generative AI, and Agentic AI frameworks to design, build, and deploy intelligent AI solutions, intelligent AI agents and scalable ML systems that integrate with enterprise application. The ideal candidate will combine strong fundamentals in ML modeling with hands-on experience in LLM integration, RAG pipelines, AI agents, and production-grade ML deployment. Additionally, S/HE should be a team player with strong communication skill and good command of English. Key Responsibilities Build, train, and deploy advanced ML and AI models (supervised, unsupervised, deep learning) Design and implement LLM-powered AI agents and multi-agent workflows Develop and optimize Retrieval-Augmented Generation (RAG) pipelines Implement end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, monitoring Fine-tune models and optimize prompts for performance and cost Deploy production-grade AI systems using Docker, Kubernetes, and cloud platforms Monitor models for drift, bias, hallucinations, and performance degradation Collaborate with product and engineering teams to translate business problems into AI-driven solutions Core ML & AI Expertise (Must have) Machine Learning Regression, Classification (Logistic, Random Forest, XGBoost, LightGBM) Clustering (K-Means, DBSCAN) Time Series (ARIMA, Prophet, LSTM) Anomaly Detection (Isolation Forest, One-Class SVM) Recommender Systems Deep Learning & NLP CNNs, RNNs, LSTM, Transformers BERT and Transformer-based architectures Embeddings and semantic search Generative AI & Agentic Systems LLM API integration (OpenAI, Anthropic, AWS Bedrock) Prompt engineering (few-shot, chain-of-thought, structured prompting) LLM fine-tuning and evaluation Agent frameworks: LangChain, LlamaIndex, AutoGen, CrewAI Tool calling & function execution frameworks Vector databases (Pinecone, Weaviate, FAISS) Hybrid search (semantic + keyword) Technical Stack Languages: Python (Primary), SQL ML Frameworks: Scikit-learn, TensorFlow, PyTorch API Frameworks: FastAPI / Flask MLOps: MLflow, CI/CD pipelines, model monitoring Cloud: AWS (Bedrock, SageMaker), Azure OpenAI, GCP AI Infrastructure: Docker, Kubernetes Required Qualifications 4-5 years of demonstrated experience in Data Science / ML 12+ years of hands-on experience with LLM-based systems Strong background in statistics, probability, and model evaluation Experience deploying AI systems in production environments Preferred Skills (Good to have) Experience with open-source LLMs (Llama, Mistral) Knowledge of RLHF / alignment techniques Experience building AI copilots or enterprise automation agents Exposure to Responsible AI, governance, and security ** Candidates who meet the above criteria and are available to join immediately will be given preference. Location: Kolkata (On-site) Experience: 4-5 Years Education: Masters degree in computer science /engineering/ data science Employment Type: Full-Time Role Overview We are seeking a highly skilled Data Scientist with expertise in Large Language Models (LLM), Machine Learning, Generative AI, and Agentic AI frameworks to design, build, and deploy intelligent AI solutions, intelligent AI agents and scalable ML systems that integrate with enterprise application. The ideal candidate will combine strong fundamentals in ML modeling with hands-on experience in LLM integration, RAG pipelines, AI agents, and production-grade ML deployment. Additionally, S/HE should be a team player with strong communication skill and good command of English. Key Responsibilities Build, train, and deploy advanced ML and AI models (supervised, unsupervised, deep learning) Design and implement LLM-powered AI agents and multi-agent workflows Develop and optimize Retrieval-Augmented Generation (RAG) pipelines Implement end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, monitoring Fine-tune models and optimize prompts for performance and cost Deploy production-grade AI systems using Docker, Kubernetes, and cloud platforms Monitor models for drift, bias, hallucinations, and performance degradation Collaborate with product and engineering teams to translate business problems into AI-driven solutions Core ML & AI Expertise (Must have) Machine Learning Regression, Classification (Logistic, Random Forest, XGBoost, LightGBM) Clustering (K-Means, DBSCAN) Time Series (ARIMA, Prophet, LSTM) Anomaly Detection (Isolation Forest, One-Class SVM) Recommender Systems Deep Learning & NLP CNNs, RNNs, LSTM, Transformers BERT and Transformer-based architectures Embeddings and semantic search Generative AI & Agentic Systems LLM API