Staff Data Engineer ( Boston or Chicago )

Press Ganey is looking to hire a self-motivated Staff Data Engineer with data platform experience. The Staff Data Engineer (Platform) will play a crucial role in designing, implementing and architecting frameworks, systems and automation that support the development, deployment and observability of state-of-the-art large language models (LLMs) and generative AI solutions. This position focuses on creating scalable, reliable systems and processes that streamline the developer experience and empower analysts and data scientists. The ideal candidate will have strong foundational skills in cloud infrastructure, automation and devops practices, as well as experience implementing data pipelines and deployment automation for ML and analytical workloads. Duties & Responsibilities Design and implement processes, systems and automation to streamline the development and deployment of AI solutions. Architect robust, reliable solutions for specific AI applications using appropriate cloud-based and open source technologies. Design and automate data pipelines to deliver complex data products to power training and online inference of AI systems. Deploy ML models, LLMs and GenAI systems 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 Skills Experience with large-scale deployment tools and environments, including Docker, Kubernetes, and cloud platforms like AWS, Azure, or GCP. Experience deploying and managing a variety of database technologies. 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 data pipelines, ETL processes, and experience with distributed data frameworks like Apache Spark or Dask. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. Knowledge of conversational AI, agent-based systems, and chat interface development. Proven track record in deploying and maintaining ML and AI solutions in a production setting. Experience with version control (e.g., Git) and CI/CD tools tailored to ML workflows. Experience with MLOps. Experience with Databricks is a plus.