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.