Data & AI
Hire ML engineers who ship models, not just notebooks.
PyTorch, TensorFlow, MLOps, model serving — our nearshore ML squads bridge the gap between research and production.
Why it matters
Why ML hiring is harder than ever
Machine learning has split into two very different jobs: research-style model development (Jupyter, papers, eval harnesses) and production ML engineering (serving, monitoring, retraining, governance). Hiring one engineer who covers both — and ships — is one of the hardest searches in the market. The cost of bad hires is real: most enterprise ML projects never make it to production.
Where Machine Learning earns its keep
Computer vision: defect detection, medical imaging, document parsing
NLP and information extraction beyond what off-the-shelf LLMs offer
Forecasting, recommendation, fraud detection — classical ML done well
MLOps platforms: serving, monitoring, retraining, model registry
Why outsource
How outsourcing accelerates your ML roadmap
ML is exactly the kind of work that benefits from a flexible, deep bench rather than a permanent headcount bet.
Production-first ML
Engineers who think in terms of latency, throughput, drift, and rollback — not just F1 score.
Right tool per problem
Classical ML when it wins, deep learning when it earns its keep, LLM APIs when they fit.
MLOps that holds
Model registry, CI/CD for models, monitoring, retraining triggers — the boring stuff that matters.
Cost-aware experiments
Engineers who know when to use a $0.02 model and when to fine-tune your own.
What we ship
What our ML squads deliver
Most ML projects die in handoff. Our engineers ship end-to-end — data, model, serving, monitoring.
End-to-end model delivery
Data pipelines, training, serving, monitoring, retraining.
MLOps platforms
MLflow, Vertex AI, SageMaker — production discipline by default.
Evaluation and governance
Eval harnesses, drift monitoring, fairness checks, audit trails.
Hire ML engineers in 48 hours.
Bring an ML squad in to ship the model your team has been promising. We are ready.