AI engineers whoship to production — not just notebooks.
The market is flooded with engineers who've completed ML certifications and built demo projects on Kaggle. Finding engineers who've taken a model from notebook to production API — handling latency, accuracy monitoring, drift detection, and compliance — is a different search entirely.
If you're building AI into a real product, you need engineers who understand that the hard part isn't training the model. The hard part is everything that happens after: serving at scale, monitoring accuracy in production, handling edge cases that didn't exist in training data, and doing all of it within your compliance constraints.
AI/ML engineers with production deployment experience.
AI engineering staff augmentation for US startups.
Our AI engineers have shipped:
Retrieval-augmented generation processing 10K+ daily queries at 95% accuracy for FinTech applications.
Production machine learning for financial decision-making with real-time inference.
Intelligent workflow automation within healthcare compliance boundaries, handling 50K+ medical transcripts.
Engineers who build the model AND the infrastructure it runs on (LangChain, CrewAI, AWS, Terraform).
This isn't a list of technologies. It's a list of outcomes from engineers currently shipping production AI for US clients.
Where our AI engineers are shipping today.
Ships production AI: RAG systems processing 10K+ daily queries at 95% accuracy, ML risk models for financial decision-making, HIPAA-compliant medical chatbots handling 50K+ transcripts.
Building intelligent agents with LangChain/CrewAI to automate DevOps workflows within HIPAA-compliant infrastructure. NUST graduate with AWS Solutions Architect + ML Engineer + Terraform certifications.
Two engineers currently active on production AI workloads. Additional AI/ML capability across the roster for computer vision, data analytics, and growth engineering — see the full roster.
AI in regulated industries needs engineers who understand both.
If you're building AI for HealthTech, FinTech, or any regulated vertical, you face a dual constraint: the model needs to perform AND the data pipeline needs to comply.
Most AI engineers have never thought about:
- ▸Whether their training data contains PHI that triggers HIPAA obligations
- ▸Whether their model outputs constitute financial advice under regulatory frameworks
- ▸Whether their RAG system's retrieved documents are access-controlled appropriately
- ▸Whether audit trails capture which data influenced which model decisions
Our AI engineers have worked in these environments. They don't need your compliance team to teach them what PHI is.
Three steps to AI engineers — matched for fit, shipping from week one.
- 01
Define the AI/ML role, stack, seniority, and compliance context.
- 02
Interview 2–3 engineers with verified production AI experience.
- 03
Engineers embed into your team, tools, and codebase. We handle operations.
See the full engagement process → · See pricing → · Meet the founder →
What AI buyers want to know before they call.
Scope your AI team in 15 minutes.
Tell us the AI problem, your stack, and your compliance context. We'll come back with a shortlist of engineers who've shipped production AI — matched for fit, not speed.