AI/ML GenAI Engg

Job SummaryWe are looking for a highly skilled and motivated AI/ML Engineer with 5 years of experience in building, deploying, and maintaining machine learning models and AI-driven solutions. The ideal candidate should have a solid foundation in machine learning algorithms, deep learning, data preprocessing, and production-level model deployment. Work with large language models (LLMs) such as GPT, BERT, LLaMA, or open-source equivalents for building chatbots, summarization tools, and content generation systems. Fine-tune or prompt-engineer pre-trained LLMs for domain-specific applications. Develop generative models (text, image, or audio) using frameworks like Hugging Face Transformers, LangChain, or Diffusers. Develop and optimize deep learning models using frameworks like TensorFlow, PyTorch, or Keras. Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost. Experience with cloud platforms (AWS/GCP/Azure) and tools like SageMaker, Vertex AI, or MLflow. Experience with NLP, computer vision, or time-series modeling. Design, develop, and deploy machine learning models and pipelines for production use cases. Work with cross-functional teams including software developers to integrate ML models into scalable applications. Perform data wrangling, preprocessing, feature engineering, and exploratory data analysis. Monitor model performance and retrain/update models as needed. Apply MLOps practices for versioning, deployment, and monitoring of ML workflows. Strong understanding of supervised, unsupervised, and reinforcement learning techniques. Familiarity with Docker, Kubernetes, CI/CD pipelines for ML workflows. Excellent problem-solving skills and the ability to work in a collaborative environment. Knowledge of big data technologies (e.g., Spark, Hadoop). Job SummaryWe are looking for a highly skilled and motivated AI/ML Engineer with 5 years of experience in building, deploying, and maintaining machine learning models and AI-driven solutions. The ideal candidate should have a solid foundation in machine learning algorithms, deep learning, data preprocessing, and production-level model deployment. Work with large language models (LLMs) such as GPT, BERT, LLaMA, or open-source equivalents for building chatbots, summarization tools, and content generation systems. Fine-tune or prompt-engineer pre-trained LLMs for domain-specific applications. Develop generative models (text, image, or audio) using frameworks like Hugging Face Transformers, LangChain, or Diffusers. Develop and optimize deep learning models using frameworks like TensorFlow, PyTorch, or Keras. Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost. Experience with cloud platforms (AWS/GCP/Azure) and tools like SageMaker, Vertex AI, or MLflow. Experience with NLP, computer vision, or time-series modeling. Design, develop, and deploy machine learning models and pipelines for production use cases. Work with cross-functional teams including software developers to integrate ML models into scalable applications. Perform data wrangling, preprocessing, feature engineering, and exploratory data analysis. Monitor model performance and retrain/update models as needed. Apply MLOps practices for versioning, deployment, and monitoring of ML workflows. Strong understanding of supervised, unsupervised, and reinforcement learning techniques. Familiarity with Docker, Kubernetes, CI/CD pipelines for ML workflows. Excellent problem-solving skills and the ability to work in a collaborative environment. Knowledge of big data technologies (e.g., Spark, Hadoop).