Sr Machine Learning Operations Engineer

Pinnacol Assurance does just one thing, and does it better than anyone: provide caring workers’ compensation protection to Colorado employers and employees. And while we may be a little biased, we believe that our work shapes communities and changes lives.

We have big hearts and love big ideas. We’ve been around for more than  100 years, but don’t let that fool you. Pinnacol is committed to taking care of Colorado employers and workers in the most innovative ways. We celebrate continuous improvement, new ideas, compassion, teamwork, integrity and excellence.

With our number one priority to keep everyone safe, along with the heart of Pinnacol’s “culture of caring” to do what is right and not what is easy, we’re currently having our team members work from home. But we’re still making time for fun with virtual events like virtual painting classes, virtual yoga and Zumba classes, and virtual happy hours!

What you’ll do:

We're seeking a highly motivated Senior Machine Learning Ops Engineer to optimize the deployment, integration, and maintenance of our internal AI chat and search services within a workman's compensation data ecosystem. You'll focus on API-based serving/consumption of internal models and public large language models (LLMs) as well as the robust CI/CD pipeline framework and container-based, cloud infrastructure which supports them. Our highly collaborative data science team will work closely alongside you, with your role emphasizing the software development and deployment aspects of machine learning solutions.

What you can expect:

• Lead Model Deployment: Design, implement, and scale production-grade deployment pipelines for large language models in AI-powered chat and search applications.
• CI/CD Optimization: Enhance our continuous integration and continuous delivery processes, specifically focused on machine learning workflows and the unique demands of AI models.
• Cloud Infrastructure Expertise: Drive the development and maintenance of cloud infrastructure to support efficient model training, deployment, and continuous improvement.
• Software Development Best Practices: Apply and promote exceptional software development principles to ensure robustness, scalability, and maintainability of machine learning systems.
• Data Pipeline Enhancement: Collaborate with the data science team to streamline data pipelines, optimizing the flow of medical, claims, and policy data into our AI chat/search services.
• Team Collaboration and Knowledge-Sharing: Mentor and guide other team members on deployment strategies, tools, and techniques specific to large language models.
• Technical Documentation: Create detailed documentation of deployment processes, infrastructure, and best practices to ensure knowledge transfer and system longevity.
• Participate in code reviews and technical design discussions.
• Stay updated on cutting-edge advancements in machine learning deployment and infrastructure.
• Contribute to the team's knowledge base through workshops and presentations.

What you need to be successful:

• Strong Software Development Foundations: Proven experience with Python, SQL, and software engineering principles.
• LLM Deployment Experience: Demonstrable experience deploying LLMs in production environments.
• Cloud Proficiency: Deep understanding of container-based application deployment within a cloud architecture, ideally within Google Cloud Platform.
• CI/CD Expertise: Experience streamlining CI/CD processes for machine learning projects, including model versioning, testing, and monitoring.
• Collaborative Mindset: Ability to work effectively with data scientists, engineers, and stakeholders throughout the development lifecycle.
• Passion for AI Applications: A genuine enthusiasm for transforming complex data into powerful AI-driven chat and search tools.

Highly Desired

• Experience with containerization technologies (e.g. Docker, Kubernetes, etc.)
• Familiarity with AI infrastructure tools (e.g. Vertex AI, Kubeflow, Mlflow, etc.)
• Experience with Natural Language Processing (NLP) techniques and libraries (e.g., spaCy, Hugging Face Transformers, etc.)
• Background in the healthcare or insurance domain

Competencies

• Analytical thinking: Analyzing and synthesizing information to understand issues, identify options, and support sound decision making. 
• Strategic thi