Data Scientist

As an experienced AI/ML Engineer, you will be responsible for designing, building, and optimizing Machine Learning models for various tasks such as classification, regression, clustering, recommendation systems, and forecasting. You will also develop and implement Deep Learning models using frameworks like ANN, CNN, RNN/LSTM, and Transformers. Additionally, you will work on building and deploying Agentic AI systems which include autonomous task planning, multi-step reasoning workflows, and tool integration. Your role will involve developing LLM-based applications, writing clean production code in Python, evaluating model performance, and collaborating with engineering and product teams to solve business problems with AI-driven solutions. Key Responsibilities: - Design, build, and optimize Machine Learning models for various tasks - Develop and implement Deep Learning models using different frameworks - 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 Required Skills: - 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 - Experience with Deep Learning frameworks (TensorFlow / PyTorch / Keras) - Practical experience with LLMs and Agentic AI frameworks - Strong knowledge of model evaluation metrics and validation techniques - Experience deploying ML/DL models in production environments Nice-to-Have: - 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, you will be responsible for designing, building, and optimizing Machine Learning models for various tasks such as classification, regression, clustering, recommendation systems, and forecasting. You will also develop and implement Deep Learning models using frameworks like ANN, CNN, RNN/LSTM, and Transformers. Additionally, you will work on building and deploying Agentic AI systems which include autonomous task planning, multi-step reasoning workflows, and tool integration. Your role will involve developing LLM-based applications, writing clean production code in Python, evaluating model performance, and collaborating with engineering and product teams to solve business problems with AI-driven solutions. Key Responsibilities: - Design, build, and optimize Machine Learning models for various tasks - Develop and implement Deep Learning models using different frameworks - 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 Required Skills: - 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 - Experience with Deep Learning frameworks (TensorFlow / PyTorch / Keras) - Practical experience with LLMs and Agentic AI frameworks - Strong knowledge of model evaluation metrics and validation techniques - Experience deploying ML/DL models in production environments Nice-to-Have: - Experience with vector databases and embedding models - Familiarity with cloud platforms (Azure / AWS / GCP) - Knowledge of MLOps and containerization (Docker / Kubernetes)