How to become a freelance machine learning engineer
A practical roadmap for machine learning engineers who want to freelance by deploying models, building ML APIs, improving evaluation, and finding client projects.
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Practical essays and guides for developers, on AI engineering, agentic coding, and freelance work.
A practical roadmap for machine learning engineers who want to freelance by deploying models, building ML APIs, improving evaluation, and finding client projects.
A practical guide for software developers who want to freelance, find first clients, scope projects, set rates, and build a sustainable technical client pipeline.
A practical guide for data scientists who want to freelance, find first clients, package services, price projects, and turn analytical skills into paid client work.
Learn how data engineers can start freelancing by packaging pipeline, warehouse, automation, data quality, and analytics infrastructure work into paid client projects.
Hybrid search for RAG combines BM25, dense embeddings, reciprocal rank fusion, reranking, and retrieval evaluation so your system retrieves better evidence.
Learn how data analysts can start freelancing with dashboards, reporting automation, KPI cleanup, business analysis, and practical client acquisition strategies.
Build agentic RAG from scratch with three file tools that list, grep, and read. The same loop coding agents run, applied to your own knowledge, plus the production hardening that makes it hold up.
A practical roadmap for developers, data professionals, and AI engineers who want to land freelance projects, set rates, write proposals, and build a client pipeline.
How AI is changing software development, why companies are moving from AI-enabled to AI-first to AI-native, and the two skills I focus on as writing code gets faster and cheaper.
AI wrote every line of my two-week Rust rebuild. The five skills that kept me in control are systems thinking, full stack range, clear communication, ruthless simplification, and testing.
Webhook architecture for production AI systems. Verify signatures, store events, enforce idempotency, dispatch async workers, and recover failures.
A practical architecture for a personal AI platform with webhooks, scheduled workflows, agents, durable events, and a tiered context hub.