Telmo da Silva

Technical training for industrial manufacturing teams, in a blended model: e-learning first, then Q&A sessions with up to 50 trainees for common questions, then hands-on practice split by track (frontend, backend, automation, administration) with a dedicated trainer per track.

• Close to 90 training runs and 1,800+ trainees over four years (around 23 a year, cohorts of 20 to 30), with an approximate 85% pass rate.
• Trainer for the Modeling track, hands-on configuration of the manufacturing execution system (MES), certified as an Associate Modeler.
• Automated training administration in Python: querying the participant database by edition, joining it with final exam results, and extracting the certified list, replacing manual spreadsheet filtering.

My own studio, always built alongside a full-time job. It is where I develop and run Moto Spotters and Scryve in production, and where the practice I teach comes from.

• Multi-agent adversarial auditing before every release: several AI agents instructed to break the system rather than approve it. It surfaced real vulnerabilities I had missed reviewing alone, and nothing counts as fixed without a test that reproduces the attack.
• Systems where a mistake stops being recoverable once published: protocols and payment infrastructure across several networks (Cardano, Nervos CKB, Bitcoin, EVM), in Rust.

Community platform for motorcyclists in Portugal, live in production (Next.js, PostgreSQL).

• LLM-based content moderation covering 25+ content types.
• Ride passport with organizer-verified stamps, motorcycle-club management and a peer-to-peer marketplace.

Long-form publishing platform, live in production: AI writing assistant (embedding-based plagiarism detection) and a custom micropayments system.

Induction and product training for new hires in a customer experience operation, assessing each person's readiness before they went live.

• Around 170 cohorts and 1,600+ employees trained over seven years: a new cohort every two weeks, 10 to 15 people each.
• Four-week follow-up per cohort in live production, often while already running the next one: anyone who showed a serious gap was pulled, coached, and only returned to the floor afterward.
• As Quality Team Leader, operations management with Quality Coaches, Trainers and SMEs reporting to me, across teams of up to 100 people.

Progressed from agent to SME to Quality Coach: grading contacts against four indicators (QA score, critical error rate, compliance, first-contact resolution), root-cause analysis by error category, client calibrations, and one-on-one coaching.

4 courses on Python for QA and 4 on AI Engineering (production AI APIs, RAG, evaluation & observability, agents), 76 chapters with code. I built the assessment layer myself: Python runs in the learner's browser, coding challenges are auto-graded against hidden tests, and the certificate is only issued once they pass. Each course tested end to end before publishing.