Services
AI & Machine Learning
Analytics & BI
Cloud & Infrastructure
MLOps
ML teams ship models. MLOps engineers make sure they stay shipped. We find people who've built real ML platforms and understand that deployment is the beginning of the work, not the end.
What We Deliver
Core Capabilities
We place MLOps engineers who build the infrastructure between a trained model and a reliable production system -- CI/CD for ML, monitoring, retraining pipelines, and the governance layer your compliance team keeps asking about.
CI/CD for ML
Engineers who build automated training, validation, and deployment pipelines with proper rollback -- so model updates don't require a war room.
Model Monitoring
Talent who set up drift detection, performance tracking, and alerting so you know when a model is degrading before your users do.
Feature Stores
Engineers who implement centralized feature management with Feast or Tecton, keeping training and serving data consistent and reusable across teams.
Infrastructure
People who manage GPU clusters, auto-scaling, and cost optimization for training workloads -- because ML compute bills can get out of hand fast.
Technology Stack
Tools & Technologies
Success Stories
Real-World Use Cases
Model Lifecycle Automation
An MLOps engineer we placed built automated training and deployment pipelines for a client managing dozens of models across multiple business units.
Real-Time Serving
Talent we placed designed a low-latency model serving platform for a client that needed sub-100ms inference at high throughput without downtime.
Governance Platform
An engineer we placed built a model registry with approval workflows and bias testing for a financial services client facing regulatory scrutiny.
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