Senior AI Engineer

We are seeking an experienced and talented Senior AI Engineer to join our team. In this role, you will be responsible for developing and deploying cutting-edge artificial intelligence solutions across various domains, including generative AI, conversational AI, and predictive AI.

This role also requires the ability to manage timelines and priorities effectively, demonstrate adaptability in a rapidly evolving environment, and maintain creativity and resilience when navigating ambiguity inherent in AI innovation.

AI solutions are hosted in AWS, includes EC2, S3, Lambda, Athena, DymanoDB, OpenSearch, CloudWatch, GLUE, Bedrock, SageMaker, Kendra, Amazon Q, Claude from Anthropic Titan Embeddings in AWS, Python, Langchain, and Streamlit technologies.

• Proven experience in developing and deploying AI solutions, particularly in the areas of generative AI, conversational AI, and predictive AI.
• Strong proficiency in Python and experience with AI/ML libraries such as PyTorch, NumPy, scikit-learn, TensorFlow, and Keras.
• Familiarity with LangChain and other NLP frameworks for building conversational agents and language models.
• Experience with web frameworks like Flask and FastAPI for building scalable AI-powered applications.
• Hands-on experience with AWS services like SageMaker, model deployment, and automation.
• Knowledge of data quality analysis, profiling, and cleansing techniques.
• Familiarity with production model deployment, monitoring, and optimization best practices.
• Understanding of neural network architectures and other machine learning modeling approaches.
• Strong problem-solving, analytical, and critical thinking skills.
• Excellent communication and collaboration abilities to work effectively with cross-functional teams.
• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field.

• Proven experience in building and deploying large-scale AI systems in production environments.
• Expertise in cutting-edge AI techniques such as transformer models, few-shot learning, reinforcement learning, and generative adversarial networks (GANs).
• Familiarity with distributed training frameworks like Horovod, TensorFlow Distributed, or PyTorch Distributed.
• Experience with cloud computing platforms like AWS, Google Cloud, or Microsoft Azure, and their respective AI/ML services.
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes for deploying and scaling AI applications.
• Proficiency in programming languages beyond Python, such as C++, Java, or Rust, for performance-critical applications.
• Experience with model optimization techniques like quantization, pruning, and model distillation for efficient deployment on resource-constrained devices.
• MLOps and Machine learning model building experience
• Familiarity with AI ethics and responsible AI practices, including model interpretability, fairness, and bias mitigation.
• Strong research skills, with a track record of publications or contributions to open-source AI projects.
• Experience with agile software development methodologies and DevOps practices for AI/ML projects.
• Excellent communication and presentation skills, with the ability to explain complex AI concepts to diverse audiences.