Key Responsibilities Develop and deploy machine learning, deep learning, and NLP models for various business use cases. Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment. Optimize model performance and ensure scalability in production environments. Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions. Conduct data analysis to identify trends and insights. Implement MLOps practices for versioning, monitoring, and automating ML workflows. Research and evaluate new AI/ML techniques, tools, and frameworks. Document system architecture, model design, and development processes. Required Skills Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras). Hands-on experience in building and deploying, finetuning ML/DL models in production. Good understanding of machine learning algorithms, neural networks, NLP, and computer vision. Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure). Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow. Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus. Strong analytical skills and ability to work with large datasets. Excellent communication and problem-solving abilities. Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.). Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage. Educational Qualification Bachelor's or Master's degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred Skills:- Prompt engineering, Machine Learning (ML), Python, Generative AI, Open-source LLMs, Retrieval Augmented Generation (RAG), Voice AI, TTS, STT, Amazon Web Services (AWS), AI Agents, Artificial Intelligence (AI), Agentic AI, Chatbot, Vector database, Finetuning , Large Language Models (LLM) tuning, Computer Vision, Natural Language Processing (NLP) and Deep Learning