Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
Work with an experienced ML engineer to build the skills needed to take AI from prototype to production, reliably and at scale. Possible uses of coaching sessions, but not limited to: - ML model deployment and serving infrastructure - Experiment tracking and model versioning tools - Automated deployment pipelines for machine learning - Model monitoring, drift detection, and alerting - Feature store design and data pipeline automation - Cost optimization and inference efficiency at scale - AI code review on your production ML systems
A clear understanding of the MLOps lifecycle from experiment tracking to production monitoring
Hands-on knowledge of key tools and platforms used by ML engineering teams
The ability to design monitoring systems that catch model and data issues early
Skills to build and maintain production ML systems independently
Coaching delivered via live sessions and .
Services included:
Technical Skills
AI Research
Model Development
ML Ops & Deployment
AI Code Review
Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
Get help with AI Fundamentals, Prompt Engineering, and .

Joined June 2026
Build production-ready AI and ML systems
I bring hands-on experience in AI, machine learning, data science, and production-grade GenAI engineering, with a focus on building practical solutions that solve real business problems. My experience includes designing and implementing AI-powered applications, RAG systems, LLM-based analytics tools, and agentic solutions that help teams automate workflows, retrieve knowledge, and generate insights from enterprise data. I have built agentic AI solutions using different frameworks and implemented them in production environments. I have worked across the full lifecycle, from problem definition and prototype development to model integration, evaluation, deployment, and scaling. My experience also includes implementing key production components such as memory, observability, traceability, human-in-the-loop workflows, evaluation, and guardrails. I can help customers understand not only the concepts behind AI and ML engineering, but also the practical architecture and implementation steps needed to move from prototype to reliable production-ready AI systems.
5h+ of coaching