Machine Learning and AI Engineer

Thermal Works LLC is seeking an ML/AI Engineer to join their AI and Data Analytics group, focusing on enhancing internal AI and ML tools. The role involves developing machine learning models, designing data pipelines, and collaborating with various teams to implement AI/ML solutions.

Responsibilities:

• Develop machine learning models related to equipment telemetry
• Deploy ML/DL Models into cloud-based access for internal use
• Design data pipelines to ingest, clean, and analyze sensor, operational, and service data
• Continuously refine models based on real-world equipment performance
• Design and build supportive AI tools for the service, operations, and engineering team
• Translate engineering formulas, design rules, and constraints into reliable, user-friendly digital tools
• Collaborate with engineers to ensure accuracy, usability, and maintainability
• Leverage Agentic AI to operationalize the AI project development to usable and actionable documentation for related teams
• Assist the AI/ML team in developing the AI digital twin, building components and design where applicable
• Provide knowledge and understanding of ML behaviors relative to AI digital twins
• Assist and provide codebase walkthroughs on digital twin code to ensure the system is well-engineered
• Work with subject matter experts to design simulations for the digital twin simulation environment
• Support additional data and digital tool initiatives as required by the business
• Evaluate new opportunities where advanced analytics can improve products, services, or operations
• Contribute to long-term data strategy at ThermalWorks
• Be able to lead a team of junior developers in additional projects

Requirements:

• Degree in Computer Science, Engineering, Data Science, or a related technical field
• Strong experience in Python and applied machine learning
• Ability to translate engineering or operational problems into practical software tools
• Strong collaboration skills across engineering, service, and business teams
• Comfort working on multiple projects with varying stakeholders
• Experience with predictive maintenance or industrial/physical systems
• Familiarity with time-series data and sensor data analysis
• Experience with web-based tools, APIs, or engineering calculation software
• Knowledge of ML Ops, model deployment, and monitoring
• Experience in manufacturing, industrial equipment, or engineering-driven organizations