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AI & Machine Learning
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ML Engineers
Most ML candidates can train a model. The ones we place can deploy it, monitor it, and figure out why it's drifting three months later. We screen for production chops and engineering discipline, not Kaggle rankings.
What We Deliver
Core Capabilities
We place ML engineers who know the difference between a model that scores well in a notebook and one that holds up in production. They bring hands-on experience with PyTorch, TensorFlow, and the unglamorous work of making ML systems reliable at scale.
Model Development
Engineers who design, train, and validate ML models with production constraints in mind -- latency budgets, serving costs, and maintainability matter as much as accuracy.
Feature Engineering
Talent who build feature pipelines and stores so model inputs stay consistent between training and inference. No training-serving skew surprises.
Model Serving
Engineers who deploy models behind low-latency APIs with proper scaling, canary rollouts, and fallback strategies when things go wrong.
Experiment Tracking
People who set up MLflow or W&B workflows so every experiment is reproducible and every model version is traceable back to its training run.
Technology Stack
Tools & Technologies
Success Stories
Real-World Use Cases
Fraud Detection
An ML engineer we placed built a real-time transaction scoring system for a fintech client, handling tens of thousands of predictions per second with production-grade reliability.
Recommendation Engine
Talent we placed designed and deployed a personalized recommendation system that measurably increased conversion rates for an e-commerce client.
Demand Forecasting
An engineer we placed built time-series forecasting models predicting inventory needs across thousands of SKUs for a retail client that was losing money on stockouts.
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