Discover
Interview stakeholders, observe work, inspect data, and map the current state.
Toronto, Canada · Open to Solutions Engineering
I turn messy operations into systems people use.
I’m Alejandro Rosero—a customer-facing Solutions Engineer and Forward Deployed AI builder. I discover the real constraint, align stakeholders, design the solution, build the pilot, and stay through adoption and measurable results.
400+users on a deployed enterprise system
>50%faster knowledge retrieval
~2 TBsource environment structured for retrieval
4×growth in site sessions at eGearz
Operational intelligence · live
See the whole system. Move the right signal.
The model changes with the decision: demand, capacity, process load, or deployment footprint. Every point represents a piece of context; the moving signal is the intervention being traced through the system.
Interactive portfolio visualization—not customer production data.
Demand signal · high-intent activity
How I work
From ambiguity to an adopted system.
I work across the customer, product, and technical layers—especially where requirements are incomplete, workflows are physical or fragmented, and trust matters as much as the build.
Prioritize
Find the constraint, establish a baseline, and translate pain into requirements.
Prototype
Build a tailored demo or pilot around a real decision and measurable outcome.
Implement
Connect systems, coordinate delivery, document workflows, and onboard users.
Adopt
Measure use, close gaps, keep humans in control, and iterate from evidence.
Selected work
Evidence, not AI theatre.
Projects are labeled by actual stage: deployed outcomes, contracted work, prototypes, or active proposals. Targets are never presented as results.
Deployed · Enterprise
Knowledge retrieval for energy operations
Led discovery, architecture, implementation, onboarding, roadmap communication, documentation, and handoff for an OCR-to-RAG knowledge platform spanning engineering, compliance, and operational content.
- Python ingestion, PyMuPDF, Tesseract OCR, metadata extraction, FAISS, and RAG
- 591 initial technical PDFs; source environment grew to approximately 2 TB
- Designed around human verification and traceable source retrieval
~400 users>50% faster retrieval
Client name withheld for confidentiality. Supplier-platform evolution is described separately from deployed outcomes.
Read full case study ↗Add proof asset
Product screenshot, architecture excerpt, or anonymized workflow image.
Product screenshot, architecture excerpt, or anonymized workflow image.
Supplier operations platform
Contributed to the evolution of the enterprise knowledge work toward supplier onboarding, purchasing, field validation, invoicing, compliance, document control, and SAP Business One integration.
~1,800 suppliers8 modules9 roles
Target operating scope from requirements—not a claimed production outcome.
Read full case study ↗Industrial process optimization
Audited a 55,000-bag-per-day production environment, analyzed PLC and checkweigher data, modeled closed-loop control in Python, and translated technical findings into an operational and financial case.
55K bags/dayCOP $1B+ / line / year
Savings reported by Cosmolac after implementation; calculation and direct attribution require final independent verification.
Read full case study ↗FisioFES clinic operating system
Audited booking, retention, capacity, no-shows, and member journeys across physiotherapy, Pilates, and training. Translated evidence into CRM stages, measurement, redesigned journeys, and an AI-assisted WhatsApp booking and follow-up prototype with human escalation.
Operational discoveryCRM architectureAI agent prototype
Read full case study ↗GovGizmo qualification pilot
Mapped the government-contracting journey and designed a 4–6 week pilot for explainable opportunity matching, source-grounded solicitation analysis, eligibility and evidence gaps, compliance extraction, bid/no-bid support, and human review.
Tailored demoPilot economicsTrust metrics
Active partnership proposal; no contract-win or production result claimed.
Read full case study ↗eGearz connected service journey
Connected Shopify commerce, bookings, product guidance, AI-assisted Q&A, POS, and repair workflows into one digital-to-operational customer journey.
297 → 1,201 sessions~3% conversion
Read full case study ↗Practice Health Check
Improved onboarding and scheduling experiences and supported evidence-led communication experiments for a healthcare practice operations business.
~25% fewer support issues~18% email lift
Read full case study ↗Why Euna
The bridge between a complex product and public impact.
Euna helps public institutions maximize limited resources through financial, procurement, grant, payment, and supplier workflows. My work fits the point where customer reality must become a credible solution, implementation plan, and adopted outcome.
Translate complexity
I audit real workflows, interview technical and nontechnical stakeholders, surface constraints, and convert findings into requirements and measurable value.
Make value tangible
I create tailored prototypes, executive proposals, architecture views, business cases, and pilot plans that connect features to customer outcomes.
Stay through adoption
I build and connect software, AI, data, and workflow layers; document the system; onboard users; preserve human review; and improve from observed use.
Capabilities
Customer-facing, technically hands-on.
A hybrid background in automation, product design, business analysis, AI engineering, and implementation lets me move between executives, operators, end users, and builders.
DiscoveryRequirements mappingSolution architectureTailored demosExecutive proposalsPilot designImplementationOnboardingHuman-in-the-loop UXPythonSQLJavaScriptNext.jsRAGOCRFAISSLLM agentsn8nREST APIsFigmaGit & Linux
Founder & Solutions Engineer · Rosero Works
Independent client work across energy, manufacturing, healthcare, wellness, hospitality, procurement intelligence, and service operations.
Digital Solutions & Operations Manager · eGearz
2024–2025 · Connected commerce, service, repair, booking, AI-assisted guidance, and operations.
Marketing & Design Intern · Practice Health Check
2022–2023 · Healthcare onboarding, scheduling, UX, and experimentation.
Education
Electromechanical Engineering Technician — Automation & Robotics, 2024 · MIT xPRO, Designing & Building AI Products and Services, 2024 · University of Toronto SCS coursework in data science, statistics, and AI.
Partner perspective
Don’t take my word for it.
This section is ready for verified client and stakeholder references. The placeholders show exactly what each testimonial should prove without inventing a quote.
“Describe Alejandro’s ability to understand an ambiguous operational problem, ask the right questions, and translate it into a clear solution.”
01
[Executive stakeholder]
[Title · Company · LinkedIn/source]
[Title · Company · LinkedIn/source]
“Describe what Alejandro personally designed or implemented, how he collaborated with the team, and why the result earned trust.”
02
[Implementation partner]
[Title · Company · Approved evidence]
[Title · Company · Approved evidence]
“Describe the measurable improvement, adoption, time saved, revenue protected, or decision quality created by the work.”
03
[Client or end user]
[Title · Company · Measurement/source]
[Title · Company · Measurement/source]
Evidence to add
Designed for stronger proof as assets become available.
These spaces are intentionally reserved. Replace them with approved, anonymized evidence—never confidential customer data.
Portrait
Professional headshot of Alejandro
Recommended: 4:5, high resolution
Professional headshot of Alejandro
Recommended: 4:5, high resolution
Enterprise system
Anonymized product screenshot or architecture diagram
Anonymized product screenshot or architecture diagram
Client proof
Approved testimonial, recommendation, or implementation photo
Approved testimonial, recommendation, or implementation photo
Let’s talk
Built to earn trust—and then do the work.
Toronto, Ontario · English C1 · Spanish native · Canadian PGWP valid through July 17, 2027.