Artificial Intelligence & Machine Learning_US Shift_8+ yrs

The candidate should have experience in AI Development including experience in developing, deploying, and optimizing AI and Generative AI solutions. The ideal candidate will have a strong technical background, hands-on experience with modern AI tools and platforms, and a proven ability to build innovative applications that leverage advanced AI techniques. You will work collaboratively with cross-functional teams to deliver AI-driven products and services that meet business needs and delight end-users. Key Prerequisites  Experience in AI and Generative AI Development  Experience in Design, develop, and deploy AI models for various use cases, such as predictive analytics, recommendation systems, and natural language processing (NLP).  Experience in Building and fine-tuning Generative AI models for applications like chatbots, text summarization, content generation, and image synthesis.  Experience in implementation and optimization of large language models (LLMs) and transformer-based architectures (e.g., GPT, BERT).  Experience in ingestion and cleaning of data  Feature Engineering and Data Engineering  Experience in Design and implementation of data pipelines for ingesting, processing, and storing large datasets.  Experience in Model Training and Optimization  Exposure to deep learning models and fine-tuning pre-trained models using frameworks like TensorFlow, PyTorch, or Hugging Face.  Exposure to optimization of models for performance, scalability, and cost efficiency on cloud platforms (e.g., AWS SageMaker, Azure ML, Google Vertex AI).  Hands-on experience in monitoring and improving model performance through retraining and evaluation metrics like accuracy, precision, and recall. AI Tools and Platform Expertise  OpenAI, Hugging Face  MLOps tools  Generative AI-specific tools and libraries for innovative applications. Technical Skills 1. Strong programming skills in Python (preferred) or other languages like Java, R, or Julia. 2. Expertise in AI frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face. 3. Proficiency in working with transformer-based models (e.g., GPT, BERT, T5, DALL-E). 4. Experience with cloud platforms (AWS, Azure, Google Cloud) and containerization tools (Docker, Kubernetes). 5. Solid understanding of databases (SQL, NoSQL) and big data processing tools (Spark, Hadoop).