AI/ML & Data Science Engineer

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.