AI Engineer - Global Analytic

Key Responsibilities:

• 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.

Desired Skills:

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field from a reputable institution.

• 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.

• 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.