AI ML Engineers

You are a talented and innovative AI/ML Engineer with a strong background in machine learning and deep learning. You have a keen interest in Generative AI (GenAI) and are excited to explore, implement, and scale GenAI models. Your primary responsibilities include designing and deploying end-to-end AI/ML solutions, experimenting with cutting-edge GenAI frameworks and tools, and collaborating with cross-functional teams to solve business problems using ML solutions. Your key responsibilities involve designing and developing robust ML models, including traditional and deep learning approaches. You will also build, train, fine-tune, and deploy Generative AI models for various use cases such as text, image, or code generation. Utilizing AWS services like SageMaker, Lambda, EC2, S3, and Glue to build scalable AI/ML solutions is an essential part of your role. Additionally, you will create and automate ML pipelines using CI/CD and MLOps best practices, conduct data preprocessing, feature engineering, and EDA for model development, and ensure model performance, fairness, and explainability throughout the lifecycle. To excel in this role, you should hold a Bachelor's/Masters degree in Computer Science, AI/ML, Data Science, or a related field and have at least 3 years of hands-on experience in machine learning and deep learning using Python with TensorFlow, PyTorch, Hugging Face, etc. Strong hands-on experience with AWS AI/ML services, experience in deploying LLMs or transformer-based models in production, and familiarity with GenAI tools like Hugging Face Transformers, LangChain, OpenAI API, Bedrock, or LLamaIndex are required. You should also possess working knowledge of REST APIs, microservices, and containerized deployments, proficiency in MLOps tools such as MLflow, SageMaker Pipelines, or Kubeflow, and strong communication skills to present complex ML concepts clearly. Preferred qualifications include experience with prompt engineering, fine-tuning, or RAG techniques, AWS Machine Learning Specialty certification or equivalent, exposure to NLP, computer vision, or multi-modal GenAI models, and contributions to open-source GenAI projects or research. Stay updated with the latest trends in GenAI and propose innovative solutions using LLMs or transformer-based architectures to drive continuous improvement in AI/ML solutions.,