Staff AI/ ML Engineer

Press Ganey is looking to hire a self-motivated Staff ML Engineer with NLP and GenAI experience. The Senior Machine Learning Engineer will play a crucial role in designing, deploying, and enhancing state-of-the-art large language models (LLMs) and generative AI solutions. This position focuses on creating intelligent, interactive, and scalable systems using agentic frameworks and chat interfaces to deliver a seamless user experience. The ideal candidate will have a strong background in natural language processing, deep learning, and deployment practices, as well as experience building robust machine learning (ML) systems that power conversational AI applications.Duties & ResponsibilitiesDesign and deploy LLMs and GenAI systems, optimizing for scalability, performance, and response quality.Deploy machine learning models into production, ensuring reliability, efficiency, and scalability across cloud or hybrid environments.Build and maintain robust CI/CD pipelines tailored to ML model lifecycle management, ensuring a streamlined and agile deployment process.Monitor model performance, identify potential improvements, and integrate feedback loops for continuous learning and adaptation.Integrate models with chat interfaces and conversational platforms to create responsive, user-centric applications.Investigate and implement agent-based architectures that support conversational intelligence and interaction modeling.Collaborate with cross-functional teams to design AI-driven features that enhance user experience and interaction within chat interfaces.Work closely with data scientists, product managers, and engineers to ensure alignment on project goals, data requirements, and system constraints.Mentor junior engineers and provide guidance on best practices in ML model development, deployment, and maintenance.Create and maintain comprehensive documentation for model architectures, code implementations, data workflows, and deployment procedures to ensure reproducibility, transparency, and ease of collaboration.Technical SkillsExperience with large-scale deployment tools and environments, including Docker, Kubernetes, and cloud platforms like AWS, Azure, or GCP.Experience deploying ML models at scale and optimizing models for low-latency, high-availability environments.Strong programming skills in Python and proficiency in libraries such as NumPy, Pandas, and Scikit-learn.Experience with machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. Familiarity with data pipelines, ETL processes, and experience with distributed data frameworks like Apache Spark or Dask.Knowledge of conversational AI, agent-based systems, and chat interface development.Proven track record in working with LLMs and conversational interfaces in a production setting.Experience with version control (e.g., Git) and CI/CD tools tailored to ML workflows.Experience with MLOps.Familiarity with Databricks is a plus.