ML/AI & LLM Ops
You're here because integrating AI is no longer optional, but turning a Jupyter notebook into a production-ready, scalable service is a massive challenge. You need engineers who understand the full lifecycle of ML/LLM systems, from data to deployment to evaluation.
Key Roles
- ML Lead
- Applied Scientist
- MLOps/LLMOps
- Prompt/RAG Engineer
- Evaluation Engineer
Core Skills & Technologies
Skills
Our Evaluation Approach for ML/AI & LLM Ops
For roles in ML/AI & LLM Ops, we understand that "good enough" is a recipe for disaster. Our Axiom Cortex™ evaluation goes beyond simple coding tests to de-risk your hiring decision.
- Deep assessment of ML fundamentals and practical application of modeling techniques.
- For LLMOps, a strong focus on RAG architecture, vector databases, and retrieval strategies.
- Practical exercises in building and deploying a model as a scalable service, including containerization and monitoring.
- Evaluation of prompt engineering, fine-tuning, and evaluation strategies for LLMs, including safety and bias mitigation.
This means you get a candidate who is not only technically proficient but is also a proven problem-solver, a strong collaborator, and ready to contribute from day one. You're not just hiring a resume; you're hiring a pre-validated, high-impact team member whose "mental shape" has been mapped to the specific demands of the role.
Ready to Hire Elite ML/AI & LLM Ops Talent?
Stop sifting through unqualified resumes. Let us provide you with a shortlist of 2-3 elite, pre-vetted candidates ready to make an impact.
Book a No-Obligation Strategy Call