Job Description

Role Overview

We're seeking a hands-on AI/ML Engineer with a passion for solving real-world problems using cutting-edge technologies. This role focuses on building and deploying solutions using large language models (LLMs), traditional machine learning techniques, and data-centric workflows. You'll work closely with cross-functional teams to design, test, and scale AI tools that deliver measurable business value.

Ideal candidates will bring curiosity, clarity, and creativity to their work-whether they've built prototypes with tools like ChatGPT, cleaned messy datasets using Python, or explained complex AI concepts in simple terms to non-technical colleagues.

Key Responsibilities

AI Solution Design & Development

• Design and iterate prompt structures and workflows using LLMs and other AI tools.
• Build ML models using techniques like XGBoost, decision trees, and regression models.
• Collaborate with business and technical stakeholders to refine AI behavior.
• Analyze model performance, resolve edge cases, and enhance output reliability.
• Document model assumptions, risks, and behaviors in a transparent, testable format.

Data Preparation & Integration

• Extract and prepare structured and unstructured data (e.g., PDFs, CAD metadata).
• Clean, transform, and validate datasets to optimize model performance.
• Integrate AI outputs into existing tools and workflows.

Deployment & Testing

• Package and test models using Python, Docker, Conda, and Jupyter.
• Support deployment into cloud environments and user-facing applications.

Qualifications

Minimum Requirements

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience).
• Strong Python skills and experience with LLMs or generative AI.
• Familiarity with ML techniques such as XGBoost, decision trees, or regression.
• Experience with data cleaning, parsing, and transformation.
• Comfortable working in cross-functional teams.

Preferred Qualifications

• Hands-on experience with LLMs (e.g., OpenAI, Anthropic, Hugging Face).
• Experience in manufacturing or engineering-adjacent environments.
• Exposure to cloud platforms (e.g., AWS, Azure) and API integration.
• A curious mindset and a track record of iterating quickly on real-world use cases.
• Ability to clearly explain AI concepts to non-technical audiences.

What We Value

We're especially interested in candidates who can:

• Share examples of AI or automation projects they've built or tested.
• Demonstrate how they've applied ML in production or pilot settings.
• Show enthusiasm for solving problems and learning from feedback.

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