AI/ML engineering built for production, not just a leaderboard score.
Model training, MLOps, and deployment pipelines that keep working long after the first demo.
Get Started with AI/ML Engineering
Free 30-min strategy call. I'll review your project and respond within 24 hours.
50+ founders consulted last month
A model that’s 95% accurate in a notebook and never makes it to production is worth nothing. I build the full pipeline — training, deployment, monitoring — so ML actually ships.
What you get
Every engagement is built around measurable outcomes — not just deliverables.
Model training & tuning
Classical ML and deep learning models trained and validated against your real data.
MLOps & deployment
CI/CD for models, versioning, and reproducible training pipelines.
Monitoring & drift detection
Automated alerts when model performance degrades in production.
API-first integration
Models exposed as clean APIs your product and engineering teams can consume directly.
The pipeline is the product
Training a model is a small part of the job. The pipeline that retrains it, the monitoring that catches drift, and the API that serves it reliably — that’s where most ML projects actually fail. I build all of it, not just the model.
What’s included
- Model training and tuning (classical ML and deep learning)
- CI/CD for models, versioning, and reproducible pipelines
- Drift detection and automated retraining
- Clean, documented APIs for your product team
From kickoff to results
A clear, transparent process — no surprises.
Problem framing
Translate a business goal into a measurable ML problem with a clear baseline.
Model development
Iterate on features, architectures and validation splits until the metric holds up.
Deployment pipeline
Containerize, version, and deploy with monitoring and rollback built in.
Ongoing tuning
Retrain on schedule or on drift signal, and keep the model aligned with the business.
01What ML frameworks do you use?
scikit-learn, PyTorch, and XGBoost, depending on the problem — I pick the simplest thing that works reliably.
02Can you deploy on our existing cloud?
Yes — AWS, GCP, or Azure; I fit into your existing infrastructure rather than forcing a new stack.
03Do you do computer vision / NLP work?
Yes, both — including fine-tuning smaller models where a full LLM isn’t the right tool.
04How do you avoid model rot?
Drift monitoring, scheduled retraining, and a documented pipeline your team can run without me.
Ready to get started?
Book a free 30-minute strategy call. No pitch, no pressure — just honest advice on where to focus.