Robert Half

Compensation

Full Description

Robert Half is hiring! We are looking for an experienced AI Engineer to join our team. In this role, you will play a pivotal part in developing cutting-edge machine learning solutions that enhance our SaaS platform and drive innovation. You will collaborate with cross-functional teams to create scalable AI systems that improve user experiences, automate operations, and expand product capabilities. Responsibilities: • Design and implement machine learning models, including predictive analytics and generative AI features, tailored for a modern SaaS platform. • Build and optimize AI pipelines for data preparation, feature engineering, model training, evaluation, and deployment. • Collaborate with product, data, and engineering teams to identify AI-driven opportunities that address business challenges. • Develop cloud-based solutions using APIs, microservices, and containerized infrastructure to integrate machine learning models. • Research and evaluate third-party AI tools and frameworks to enhance productivity and system functionality. • Ensure AI systems adhere to privacy, security, and reliability standards while maintaining fairness and accuracy. • Document workflows, maintain experiment logs, and establish reproducible processes for AI development. • Stay informed about advancements in machine learning, generative AI, and SaaS architecture to apply best practices in projects. • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. • Minimum of 3 years of experience deploying production-grade machine learning models. • Proficiency in Python and familiarity with frameworks such as PyTorch, TensorFlow, or scikit-learn. • Hands-on experience with cloud platforms like AWS, Google Cloud, or Azure, and containerization tools such as Docker or Kubernetes. • Knowledge of large language models (LLMs), vector databases, and modern AI/ML technologies. • Understanding of microservice architectures and development processes. • Strong foundation in data structures, algorithms, and software engineering principles. • Experience with experimentation frameworks and tools for monitoring model performance.