Senior AI/ML Engineer

Design, develop, and deploy scalable machine learning models and AI solutions for Provation's products. Implement best practices in AI/ML model development, including data preprocessing, feature engineering, model training, and optimization. Work closely with Product Management, Data Science, and Engineering to evaluate, develop, and maintain innovative AI-powered solutions. Manage and scale data pipelines to support AI/ML workflows, ensuring efficient data availability for training and inference. Research and implement state-of-the-art AI/ML techniques, including deep learning, NLP, computer vision, and generative AI. Develop and manage MLOps workflows, ensuring model monitoring, retraining, and governance in production. Collaborate with DevOps and software engineering teams to deploy AI models in cloud environments including integration of AI/ML models with .NET microservices and Kubernetes Ensure compliance with security, ethical AI, and regulatory requirements (e.g., HIPAA, GDPR) when working with sensitive data. Develop and implement methods to measure the accuracy and effectiveness of AI solutions, ensuring continuous improvement and adherence to performance standards. Participate in Agile development processes, including daily stand-ups, sprint planning, and code reviews. Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent experience. Proven experience as an AI/ML Engineer, Data Scientist, or related role with a strong focus on developing AI/ML-driven solutions. Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Experience with generative AI and large language models (LLMs), including their development, deployment, and optimization. Hands-on experience in building and optimizing deep learning models for NLP, computer vision, or time-series forecasting. Experience working with cloud-based AI/ML platforms (Azure ML, Azure AI Foundry) Solid understanding of data structures, algorithms, and distributed computing. Knowledge of MLOps principles and experience with tools such as MLflow, Kubeflow, or Airflow. Experience with C# and .NET Experience deploying AI/ML models in containerized environments (Docker, Kubernetes) is a plus. Strong understanding of AI ethics, data privacy, and security considerations.