Job
Description
We are looking for an experienced AI/ML & Data Science Engineer to develop and optimize machine learning models, deep learning architectures, and generative AI solutions. The ideal candidate will have hands-on experience in designing, training, and deploying AI-driven systems while leveraging data science techniques to derive meaningful insights. Key ResponsibilitiesMachine Learning & Deep Learning Design, develop, and implement machine learning and deep learning models for various applications. Optimize model performance using techniques like transfer learning, hyperparameter tuning, and pruning. Work with frameworks such as TensorFlow, PyTorch, Scikit-Learn, and Hugging Face. Build and deploy AI models into production environments using cloud platforms (AWS, Azure, GCP). Generative AI & NLP Develop and fine-tune large language models (LLMs) like GPT, BERT, T5, and Stable Diffusion. Work with transformers, RNNs, and CNNs for text, image, and multimodal AI applications. Implement and optimize prompt engineering techniques for GenAI applications. Develop AI-driven content generation, chatbots, and virtual assistants. Data Science & Analytics Collect, preprocess, and analyze structured and unstructured datasets. Perform feature engineering, anomaly detection, and statistical analysis. Use data visualization tools (Matplotlib, Seaborn, Tableau, Power BI) to present insights. Work with large datasets using SQL, Pandas, and Apache Spark. Required Skills & Qualifications Bachelors/Master’s/Ph.D. in Computer Science, Data Science, AI/ML, or related fields. Proficiency in Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch). Experience with Generative AI frameworks (Hugging Face, OpenAI API, Stable Diffusion). Knowledge of data pipelines, cloud computing (AWS Sagemaker, Azure ML, GCP AI). Experience in model optimization, explainable AI (XAI), and interpretability. Strong understanding of probability, statistics, and mathematical optimization. Hands-on experience with version control (Git), containerization (Docker), and orchestration (Kubernetes). Excellent problem-solving skills and the ability to work in an agile environment. Nice-to-Have Skills Experience in Reinforcement Learning (RL) and Graph Neural Networks (GNNs). Exposure to synthetic data generation and data augmentation techniques. Familiarity with AI governance, ethical AI, and compliance standards. Knowledge of edge AI and on-device ML.
