Services
ML engineering advisory for teams building under real constraints.
Engagements focus on architecture, evaluation, reliability, performance, and the responsible deployment decisions that determine whether an AI system can scale.
Primary practice
ML engineering engagements
ML Systems Advisory
Architecture, evaluation, and scaling guidance for teams building recommendation, ranking, NLP, and agentic AI systems.
Discuss this engagementPerformance & Reliability Audit
Practical assessment of inference cost, latency, monitoring, failure modes, and the operating discipline around production ML.
Discuss this engagementModel Evaluation Review
Independent review of metrics, datasets, regressions, edge cases, and the evidence used to decide whether a model is ready.
Discuss this engagementExecutive Briefings
Clear, technically grounded briefings for leaders making decisions about AI architecture, delivery, risk, and investment.
Discuss this engagementEngagement shape
Built around decisions, not decks.
Intake
The technical question, stakeholder context, constraints, and decision deadline.
Review
Data, metrics, architecture, model behavior, reliability, and production readiness.
Plan
A concise engineering recommendation with tradeoffs, evidence gaps, and next actions.
Selective individual work
Career advisory for ML and AI practitioners
Individual services remain available for engineers who need rigorous preparation, positioning, and negotiation guidance.
Mock Interviews (ML/AI)
Rigorous technical mock interviews (System Design, Coding, Theory) with detailed feedback.
Career Mentorship
1:1 coaching for engineers transitioning into AI or aiming for Staff/Principal roles.
Resume & Portfolio Review
Strategic positioning of your projects and experience to attract top-tier tech companies.
Offer Negotiation & Comp Strategy
Maximize your total compensation with data-driven negotiation tactics for base, equity, leveling, and competing offers.
Start with the decision