AI Savant
A specialist role for someone who lives and breathes artificial intelligence — and can prove it through real-world systems, models, and impact.
We’re looking for an experienced AI engineer to take ownership of intelligent systems from concept through to deployment. You’ll work directly with the founder, building and scaling AI-driven solutions across a portfolio of projects. No hand-holding — just clear outcomes, a sharp process, and room to grow.
$45–$85 per hour (based on experience) · Flexible contractor arrangement · Remote-first
Key Responsibilities
You’ll own the full AI lifecycle — from problem definition and model design through to deployment, optimization, and performance monitoring.
Design, build, and deploy intelligent systems that solve real-world problems.
- Develop and train machine learning / deep learning models
- Build and integrate AI features into web or software applications
- Optimize model performance, scalability, and efficiency
- Work with APIs, LLMs, and AI frameworks (e.g., OpenAI, Hugging Face)
- Deploy models into production environments
Turn raw data into actionable AI-driven insights.
- Prepare, clean, and structure datasets for training
- Select appropriate algorithms and architectures
- Fine-tune models for accuracy and performance
- Evaluate models using relevant metrics
- Continuously improve models through iteration
Translate data into decisions and communicate progress clearly.
- Build AI-powered automations and pipelines
- Integrate AI into business processes and user experiences
- Develop prompt engineering strategies for LLMs
- Ensure reliability, security, and scalability of AI systems
Skills & Attributes
We’re hiring for genuine AI expertise and professional discipline. You should be able to point to real systems you’ve built — not just tools you’ve used.
- 2+ years of hands-on experience in AI/ML engineering
- Strong proficiency in Python and AI/ML frameworks (TensorFlow, PyTorch, etc.)
- Experience working with APIs, LLMs, or generative AI tools
- Solid understanding of data structures, algorithms, and model evaluation
- Ability to build and deploy production-ready AI solutions
- Clear, professional communication
- Reliable, self-directed, and deadline-oriented
- Experience with AI agents or autonomous systems
- Familiarity with cloud platforms (AWS, GCP, Azure)
- Knowledge of MLOps and CI/CD pipelines
- Experience integrating AI into web or SaaS platforms
- Background in automation or workflow systems
- Prior agency or freelance experience
What You’ll Bring
Beyond the technical skills, this is what separates a great hire from a good one.
Ownership mentality You treat project outcomes like your own. You don’t wait to be told something is broken
Sharp attention to detail You catch what others miss — inefficient models, edge cases, or flawed outputs.
Intellectual curiosity You stay updated with AI advancements, test new approaches, and enjoy solving complex problems.
Clear communication You can explain technical AI concepts in plain English without oversimplifying.
Process discipline You follow structured workflows, document your work, and maintain quality under pressure.
AdaptabilityYou can move from model training to deployment to debugging without losing momentum.
Work Structure & Compensation
Hourly contractor role with agreed weekly hours. Flexibility provided, subject to consistent availability and output standards.
$45–$85 per hour depending on experience and demonstrated results, with clear performance-based growth paths.
Opportunity to grow into a senior or lead AI role as the company scales.
Application Process
Submit your resume, a brief cover note, and links to projects or case studies demonstrating AI systems you’ve built.
A focused 30-minute video call to assess your AI knowledge, problem-solving approach, and experience.
A short paid exercise to evaluate how you approach building or improving an AI solution.
How to Apply
Send your resume and a short written response addressing the following questions:
Share a specific AI project you’re proud of — what was the problem, what did you build, and what was the impact?
How do you approach a new AI project from scratch? Walk us through your first steps.
What’s something most AI engineers get wrong — and how do you approach it differently?
What tools, frameworks, or platforms do you use daily, and why?