Services
What I offer
Four focused areas where senior ML engineering makes a direct, measurable difference. Every engagement delivers working software, not slide decks.
Agentic Automation
LLM workflows that actually ship
Most LLM demos fail in production because they were never designed for production. I design, build, and deploy agentic pipelines that handle edge cases gracefully, stay observable, and stay within your cost budget.
Outcomes
- Production-grade document extraction, routing, and classification pipelines
- Multi-step agent orchestration with structured outputs and retry logic
- Human-in-the-loop review interfaces with audit trails
- Cost-aware model selection, caching, and prompt optimization
- Evaluation frameworks to measure accuracy before and after changes
What you get
- Working pipeline code with full documentation
- Prompt library with documented decision rationale
- Evaluation harness with golden test set
- Deployment configuration (Docker / cloud functions)
- Runbook for operations and monitoring
Recommenders & Ranking
Retrieval and ranking built for real traffic
Recommender systems are among the highest-leverage investments in consumer and B2B products. I build two-stage retrieval + ranking architectures that scale, and I integrate them with your experimentation stack so improvements are measurable.
Outcomes
- Significant uplift in engagement, click-through, or revenue metrics
- Sub-50ms retrieval at thousands of queries per second
- Graceful handling of cold-start for new users and items
- Measurable lift in A/B tests against existing baselines
- Reduced offline-to-online model performance gap
What you get
- Feature store design and implementation (or integration with existing)
- Candidate generation service with vector search integration
- Ranking model training pipeline
- Online serving API with logging for feedback loops
- A/B testing integration and metric dashboard
MLOps & Productionization
From notebook to reliable production system
Research models that never made it to production are not assets. I build the training infrastructure, serving layer, and observability tooling that converts ML experiments into reliable, maintainable systems.
Outcomes
- Reproducible, parameterized training pipelines with lineage tracking
- Low-latency model serving with autoscaling (AWS, GCP, or Kubernetes)
- Model registry with staged rollout and rollback capability
- Drift detection, alerting, and automated retraining triggers
- Significant reduction in time-to-deploy for new model versions
What you get
- Reproducible training pipeline
- Experiment tracking and artifact management
- Serving infrastructure with CI/CD and deployment automation
- Monitoring dashboards for model performance and data drift
Measurement & Experimentation
Know what's actually working
Bad measurement is expensive. Instrumented A/B tests and causal analyses replace intuition with evidence, so product teams can ship changes confidently and ML teams can claim credit for real improvements.
Outcomes
- Correctly powered experiments that answer the right question
- Reduced time-to-decision on product and model changes
- Reliable guardrail metrics that prevent regressions
- Causal estimates of impact in non-randomized settings
- Shared statistical language between data, product, and engineering
What you get
- Experiment platform design (or audit and improvement of existing)
- Statistical testing framework: frequentist, Bayesian, or sequential
- Power analysis and sample size calculator
- Metric taxonomy with primary, secondary, and guardrail metrics
- Documentation and team enablement guide
Good fit / Not a fit
Clarity upfront saves everyone time. Here's an honest read on where I add the most value.
Good fit
- You have a production ML system that isn't performing or isn't shipping fast enough.
- You need senior ML capacity for a defined period without a full-time hire.
- You're building a new AI-powered feature and want to get the architecture right from the start.
- Your team has strong software engineers but limited ML depth.
- You want rigorous measurement to validate — or invalidate — an ML investment.
- You're dealing with compute costs that have grown faster than the business.
Not a fit
- You want a data science generalist who will own analytics, dashboards, and ML.
- The engagement requires more than 20 hrs/week of dedicated capacity.
- You're in a regulated industry and need compliance-specific guidance (HIPAA, FedRAMP, etc.).
- You need a full-time team lead or people manager.
- The project is primarily about business intelligence or BI tooling.
Sounds like a fit?
Send a message with your goals and constraints. Short is fine — I'll ask follow-up questions.