MLOps Talent
Building AI models is one thing — deploying, scaling, and maintaining them in production is another. Our MLOps engineers bridge the gap between data science and production, ensuring your models run reliably, efficiently, and at scale.
Every mlops engineers we place has been vetted for production-level expertise across these core competencies.
MLOps engineers understand both machine learning and infrastructure. They optimize GPU utilization, reduce inference costs, automate retraining pipelines, and set up monitoring that catches model drift before it impacts users — skills that software engineers and data scientists rarely have together.
CI/CD for ML models — automated testing, staging, canary deployment, and rollback for safe model updates.
Provisioning, scaling, and cost-optimizing GPU clusters for LLM inference and model training.
Real-time dashboards tracking model accuracy, latency, drift, and cost — with automated alerts and retraining triggers.
Every MLOps developer passes our multi-stage assessment: MLOps-specific coding challenges, system design review, code quality audit, and cultural fit evaluation. Only the top 3% of applicants make it through. You interview pre-qualified mlops engineers — not resumes.
We present 2-3 qualified mlops engineers within 48 hours of your request. Our network includes 100+ MLOps specialists with 4+ years average experience — no waiting weeks for recruiters to source candidates.
Our mlops engineers join your Slack, your standups, and your MLOps codebase. They follow your coding standards, use your CI/CD pipeline, and attend your sprint ceremonies — fully embedded in your engineering team from day one.
We're not just a staffing agency — we've built 23+ production products including Agiled, Chatsy, and Morphed. Our mlops engineers bring that hands-on MLOps production experience to your team, not just textbook knowledge.
Only 3% of applicants make it through. Every mlops engineers we place has passed all four stages.
We review MLOps project history, GitHub contributions, open-source work, and production deployments to verify hands-on MLOps experience.
Timed coding challenges covering ML Pipeline Orchestration (Kubeflow, MLflow), Model Serving (TorchServe, vLLM, TGI), GPU Infrastructure (CUDA, NVIDIA Triton) — plus system design problems that test real-world MLOps architecture decisions.
A 60-minute live coding session where candidates build a feature using MLOps alongside our senior engineers — testing code quality, debugging skills, and communication.
Soft skills evaluation focused on async communication, sprint collaboration, and the ability to integrate into your existing engineering team from day one.
Choose the model that fits your project needs. No long-term contracts — scale up or down as your project demands.
A mlops developer works exclusively on your project, 40 hours/week. Best for ongoing product development and long-term projects.
20 hours/week of dedicated mlops development. Ideal for startups, maintenance, or projects that don't need full-time capacity.
Fixed-scope mlops development with a defined timeline and deliverables. Best for specific features, migrations, or MVPs.
Multiple mlops developers join your existing team. Best for scaling quickly when you need to ship faster.
Beyond hiring mlops engineers, we offer these related services:
Common questions about hiring mlops engineers
We present 2-3 pre-vetted mlops engineers within 48 hours. After you interview and select, developers can start within 3-5 business days.
Get matched with pre-vetted mlops engineers in 48 hours. No long-term contracts. Replacement guarantee.