Six core service areas. Each one goes deep on capabilities, deliverables, and the exact tools I use. No vague promises — just concrete engineering.
🧠
Production ML Systems
Models that don't just work in notebooks — they work at scale.
End-to-end machine learning systems from data ingestion through model serving. I build pipelines that handle real-world data messiness: missing values, schema drift, concept drift, and the thousand edge cases that break demo-quality code.
Capabilities
Supervised & unsupervised model development
Feature engineering pipelines (200+ features automated)
Model validation, A/B testing, and statistical significance
Ensemble methods and model stacking
Real-time and batch inference architectures
Model monitoring and automated retraining triggers
What You Get
Production-deployed model with CI/CD pipeline
Feature store or feature engineering documentation
Model performance dashboard with business KPI mapping
Turning customer data into revenue-generating decisions.
I architect recommendation engines that go beyond "customers also bought." Using Learning to Rank, behavioral modeling, and real-time signals, I build systems that understand customer intent and optimize for business metrics — not just accuracy scores.
Capabilities
Learning to Rank (LTR) for product/content ranking
Next Best Offer (NBO) engines for cross-sell/upsell
Collaborative + content-based hybrid approaches
Real-time behavioral signal processing
Multi-armed bandit for exploration vs exploitation
Customer segmentation and micro-targeting
What You Get
Deployed recommendation API with <50ms p99 latency
A/B testing framework with automated winner selection
Business impact report (CTR, conversion, revenue lift)
Industrial-grade visual intelligence from pixels to decisions.
From satellite imagery analysis to manufacturing defect detection, I build computer vision systems that work in production conditions — not just curated benchmark datasets. Specializing in remote sensing, object detection, and semantic segmentation.
The infrastructure that makes ML reliable, repeatable, and observable.
A model that can't be deployed, monitored, or retrained is a research project. I build the operational backbone that turns ML experiments into production systems: automated pipelines, model registries, monitoring dashboards, and CI/CD for machine learning.
Capabilities
End-to-end pipeline automation (Airflow, Vertex AI Pipelines)
Model versioning, registry, and artifact management
CI/CD for ML (automated training, validation, deployment)
Feature stores and real-time feature serving
Model monitoring (drift detection, performance degradation)
Infrastructure as Code (Terraform, CloudFormation)
From automated customer service agents to sentiment analysis pipelines, I build NLP systems that understand context, handle ambiguity, and integrate with your existing workflows. Modern LLMs are powerful, but production deployment requires careful engineering.
Building the foundation before building the models.
Sometimes the right answer isn't a model — it's better data infrastructure, clearer metrics, or a more capable team. I help organizations build data-driven cultures from the ground up: from CDP architecture to hiring and mentoring data scientists.
Capabilities
Customer Data Platform (CDP) architecture
Data maturity assessment and roadmap
KPI framework design and metric definition
Data team hiring, mentoring, and skill development
Principles that guide my work — not just technical practices, but engineering philosophy.
Production-First, Not Research-First
Every model is built with deployment in mind from day one. Latency constraints, data pipeline integration, monitoring requirements, and failure modes are designed in — not bolted on later.
Measure Everything, Assume Nothing
I don't claim a model "works" without quantified evidence. Every deliverable includes performance metrics mapped to business KPIs. If I can't measure the impact, I'll tell you upfront.
Document for the Next Person
Code without documentation is technical debt. Every project includes architecture diagrams, runbooks, and handoff guides — because the person maintaining this system might not be me.
Simplicity Over Cleverness
I choose the simplest solution that solves the problem. A well-tuned XGBoost model that's explainable and maintainable beats a black-box deep learning model that nobody can debug.
Not Sure Which Service You Need?
That's normal. Most projects span multiple areas. Let's have a 30-minute discovery call and I'll tell you exactly what's feasible — and what it'll cost.
