Data Scientist - AI/ML System

As an experienced AI/ML Engineer in the AI & Data Science department, your role will involve designing, building, and optimizing Machine Learning models for various purposes such as classification, regression, clustering, recommendation systems, and forecasting. You will also be responsible for developing and implementing Deep Learning models using architectures like ANN, CNN, RNN/LSTM, Transformers, etc., for both structured and unstructured data. Additionally, you will build and deploy Agentic AI systems, including autonomous task planning, multi-step reasoning workflows, tool integration, and orchestration. You will develop LLM-based applications and integrate them into intelligent agent pipelines, perform advanced data querying and manipulation using SQL, write clean and scalable production code in Python, evaluate model performance, and collaborate with engineering and product teams to solve business problems with AI-driven solutions. Key Responsibilities: - Design, build, and optimize Machine Learning models for various applications - Develop and implement Deep Learning models for structured and unstructured data - Build and deploy Agentic AI systems, including autonomous task planning and multi-step reasoning workflows - Develop LLM-based applications and integrate them into intelligent agent pipelines - Perform advanced data querying and manipulation using SQL - Write clean, scalable production code in Python - Evaluate model performance and implement continuous improvement strategies - Collaborate with engineering and product teams to translate business problems into AI-driven solutions Qualifications Required: - Strong hands-on experience in Python (NumPy, Pandas, Scikit-learn, etc.) - Proficiency in SQL for data extraction and transformation - Deep understanding of Machine Learning algorithms (XGBoost, Random Forest, SVM, Gradient Boosting, etc.) - Experience with Deep Learning frameworks (TensorFlow / PyTorch / Keras) - Practical experience with LLMs and Agentic AI frameworks (LangChain Agents, LlamaIndex, CrewAI, AutoGen, etc.) - Strong knowledge of model evaluation metrics and validation techniques - Experience deploying ML/DL models in production environments Nice-to-Have Skills: - Experience with vector databases and embedding models - Familiarity with cloud platforms (Azure / AWS / GCP) - Knowledge of MLOps and containerization (Docker / Kubernetes) As an experienced AI/ML Engineer in the AI & Data Science department, your role will involve designing, building, and optimizing Machine Learning models for various purposes such as classification, regression, clustering, recommendation systems, and forecasting. You will also be responsible for developing and implementing Deep Learning models using architectures like ANN, CNN, RNN/LSTM, Transformers, etc., for both structured and unstructured data. Additionally, you will build and deploy Agentic AI systems, including autonomous task planning, multi-step reasoning workflows, tool integration, and orchestration. You will develop LLM-based applications and integrate them into intelligent agent pipelines, perform advanced data querying and manipulation using SQL, write clean and scalable production code in Python, evaluate model performance, and collaborate with engineering and product teams to solve business problems with AI-driven solutions. Key Responsibilities: - Design, build, and optimize Machine Learning models for various applications - Develop and implement Deep Learning models for structured and unstructured data - Build and deploy Agentic AI systems, including autonomous task planning and multi-step reasoning workflows - Develop LLM-based applications and integrate them into intelligent agent pipelines - Perform advanced data querying and manipulation using SQL - Write clean, scalable production code in Python - Evaluate model performance and implement continuous improvement strategies - Collaborate with engineering and product teams to translate business problems into AI-driven solutions Qualifications Required: - Strong hands-on experience in Python (NumPy, Pandas, Scikit-learn, etc.) - Proficiency in SQL for data extraction and transformation - Deep understanding of Machine Learning algorithms (XGBoost, Random Forest, SVM, Gradient Boosting, etc.) - Experience with Deep Learning frameworks (TensorFlow / PyTorch / Keras) - Practical experience with LLMs and Agentic AI frameworks (LangChain Agents, LlamaIndex, CrewAI, AutoGen, etc.) - Strong knowledge of model evaluation metrics and validation techniques - Experience deploying ML/DL models in production environments Nice-to-Have Skills: - Experience with vector databases and embedding models - Familiarity with cloud platforms (Azure / AWS / GCP) - Knowledge of MLOps and containerization (Docker / Kubernetes)