Staff Data Engineer (AI)

TL;DR

Staff Data Engineer (AI): Designing and architecting frameworks, systems, and automation to support the development, deployment, and observability of large language models and generative AI solutions with an accent on scalable infrastructure and MLOps. Focus on creating robust data pipelines, automating ML model lifecycles, and ensuring high-availability production environments for AI-driven features.

Location: Must be based in or able to work from Chicago, IL or Boston, MA

Salary: $100,000–$140,000

Company

A leading provider of experience measurement and data analytics for complex industries, with deep roots in U.S. healthcare.

What you will do

• Design and implement processes and automation to streamline AI solution development and deployment.
• Architect robust, reliable solutions for AI applications using cloud-based technologies.
• Build and automate data pipelines for training and online inference of AI systems.
• Deploy ML models, LLMs, and GenAI systems into production environments.
• Maintain CI/CD pipelines tailored to ML model lifecycle management.
• Collaborate with cross-functional teams to integrate AI-driven features into user-centric applications.

Requirements

• 5+ years of experience in platform engineering with a focus on data and ML systems.
• Strong programming skills in Python and proficiency in libraries like NumPy, Pandas, and Scikit-learn.
• Experience with large-scale deployment tools including Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
• Proven track record in deploying and maintaining ML and AI solutions in production.
• Experience with distributed data frameworks like Apache Spark or Dask.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.

Nice to have

• Experience with Databricks.
• Familiarity with Hugging Face Transformers.
• Knowledge of conversational AI and agent-based architectures.

Culture & Benefits

• Commitment to a diverse, inclusive, and authentic workplace.
• Focus on continuous learning and professional growth.
• Collaborative team environment with a focus on innovation.
• Competitive benefits package.
• Eligibility for discretionary bonuses tied to results.