AI Automation & Full Stack Engineer
I build AI systems that do the work — and the platforms they live in.
Toronto, Canada
I'm a full-stack engineer who specializes in making software do the work. I've spent 10+ years shipping production software — and the last several building platforms where AI automation isn't a feature bolted on at the end, it's the engine: a voice agent that answers a business phone line 24/7 and books the appointment while the caller is still on the call, hiring assessments that score candidates across 23 industries with objective, comparable results, a discovery engine that scans 8 platforms and validates startup ideas in minutes instead of weeks, a marketplace where automation deliverables are verified by the work itself rather than by self-reported skills, and annotation pipelines that feed production ML systems with human-in-the-loop quality control.
I hold a Master's in Computer Science (AI) from the University of Jos and a Post-Graduate Certificate in Blockchain & Back-End Development from York University. I've led cross-functional teams, onboarded 12,000+ verified users across 42+ countries, and taken more than seventeen products from first commit to production. My home turf is the seam where LLM pipelines meet real infrastructure — the interface, the services behind it, and the data layer underneath.
LLM-powered pipelines that replace manual workflows end to end — voice agents that answer a live call and book the appointment on it, assessment engines with behavioral scoring, multi-platform discovery crawlers, and human-in-the-loop annotation flows.
Production web and mobile products with real-time cores — marketplaces, hiring platforms, collaboration tools. React and Next.js frontends, Node.js and Python services, WebSocket infrastructure, and payments.
The layer that makes automation trustworthy: annotation pipelines with consensus scoring, analytics dashboards, aggregation-driven recommendation engines, and API-ready dataset delivery into ML pipelines.
$ every system follows the same loop — manual work in, finished work out
Systems I've architected and shipped — the problem, the machine I built for it, and what changed.
The business phone line that answers itself
A missed call is not a missed message, it is a lost customer. Small businesses cannot staff a phone around the clock, so calls that arrive after hours or while the line is busy fall to voicemail — and the caller simply dials the next business on their list. The revenue leaves before anyone knows the call happened.
I built Rhanta to answer the business line 24/7 and carry a call all the way to a booked appointment rather than to a message someone has to action later. It picks up every call, captures the caller as a lead, and writes real appointments straight into the business's calendar while the caller is still on the phone. The same platform runs the outbound direction too — dialling the existing list back for reminders, offers, and follow-ups — so inbound capture and outbound win-back happen on one system instead of two.
The platform for AI & automation work
Companies want automation — chatbots, AI agents, workflows, data pipelines — but hiring for it is guesswork. Anyone can claim to be an automation expert, portfolios can't be verified, and paying upfront for work that may never ship burns budgets. The AI economy had no infrastructure for trust.
I architected Rework Digital as a verified marketplace purpose-built for the AI and automation economy. Builders prove their skills through real deliverables — chatbots, workflows, pipelines, integrations, and agents verified with cryptographic proof of work, not self-reported resumes. Companies post a brief for free, get matched with vetted specialists across six categories (AI chatbots & agents, workflow automation, AI integration & APIs, data pipelines & analytics, RPA & document processing, and marketing automation), and pay through milestone escrow: funds sit protected and release only when the shipped work is approved.
The hiring evaluation pipeline, fully automated
Resumes and unstructured interviews are poor predictors of job performance, and manual screening doesn't scale — companies burn thousands on bad hires because every step of evaluation depends on subjective human judgment.
I designed and built an AI assessment engine that automates candidate evaluation end to end: career-specific simulations across 23 industries, live coding sandboxes for technical roles, anti-cheating proctoring with real-time behavioral monitoring, and an analysis layer that aggregates Performance (40%), Behavioral Signals (25%), Role-Fit Intelligence (25%), and Integrity Verification (10%) into a single Hiring Confidence Score. Employers assemble multi-stage assessments in a step-by-step builder; the system handles proctoring, scoring, risk-flagging via a Regret Minimizer Report, top-performer similarity matching, and explainable AI-powered candidate comparison — replacing gut-feel screening with comparable, explainable scores.
Market research on autopilot
Validating a startup idea takes weeks of manual research, and the output is usually confirmation bias. Founders didn't need another survey tool — they needed the discovery process itself automated.
I built an AI discovery engine that scans 8 platforms simultaneously — Reddit, Twitter/X, Product Hunt, Hacker News, G2, Trustpilot, app store reviews, and Stack Overflow — and runs 7 automated analysis methods over the raw conversations: sentiment analysis, pain-point clustering, demand-signal detection, trend velocity tracking, competitor gap analysis, market sizing estimation, and opportunity scoring. Every surfaced idea ships with a validation score, TAM/SAM/SOM market sizing, and a competitor landscape breakdown — no analyst required.
Production-grade training data, humans exactly where they belong
AI teams lose more time fixing bad labels than training models. Annotation vendors are slow, inconsistent, and blind to quality — the opposite of what a production ML pipeline needs.
I built an enterprise annotation platform designed as an automation pipeline with human checkpoints: automated pre-labeling with human-in-the-loop verification, multi-tier quality assurance with annotator consensus scoring, custom taxonomy and labeling schema builders, real-time quality dashboards, and a RESTful API that delivers quality-controlled datasets straight into existing ML pipelines.
Reimagining music industry collaboration
Independent artists are locked out of the music industry's inner circle. They can upload tracks, but there's no infrastructure for real collaboration — finding a producer, getting heard by an A&R rep, or landing paid gigs. Labels scout through social media and word-of-mouth, missing talent entirely.
I built Colsphere as a three-sided platform connecting artists, labels, and fans through active participation rather than passive distribution. The web platform (React) and cross-platform mobile app (React Native) share a real-time backend powered by WebSockets. Artists create verified profiles with EPKs that auto-sync from Spotify and socials, post collaboration projects, and build a network. Labels get data-driven A&R tools — deep search with genre, location, and engagement filters, a signing suite for managing demos, and an asset vault for secure stem sharing. Fans enter the 'Sphere' to discover trending artists, vote on demos, and earn member-only perks.
Multi-vendor fashion marketplace for Africa
Africa's fashion industry is fragmented across thousands of independent designers and tailors with no unified digital marketplace. Customers struggle to discover local talent or book services reliably, while vendors lack the tools to scale beyond word-of-mouth referrals.
I led the development of a full-scale multi-vendor marketplace connecting designers, tailors, and customers on one platform: geo-location-based service discovery, vendor storefronts with product catalogs, integrated booking and payment systems, a vendor management portal, and real-time chat between buyers and sellers. I also established the brand identity and designed the end-to-end UI/UX across web and mobile.
Smaller builds — dApps, fintech, and data-driven side projects.
Eight years of shipping — from banking systems and security automation to AI platforms.
Liano
Tools I reach for when there's a workflow to automate or a platform to ship.
AI Automation Engineering — From manual workflow to production automation — build systems that do the work.
Learn to see workflows the way an automation engineer does: spot the manual work worth killing, map it as input → process → output, and decide when a problem needs AI versus plain code.
The two languages that run the automation world. Scripting, data wrangling, and the package ecosystems that let you stand on other people's shoulders.
Automation is mostly connecting systems that were never designed to talk. REST APIs, authentication, webhooks, and scheduled jobs — the plumbing behind every pipeline.
How large language models actually work — tokens, context windows, and why prompts are functions. Write prompts that return structured, machine-readable output every time.
Your first real AI integration. The Messages API, tool use, and streaming — wrapped in an endpoint that classifies real input for a real workflow.
Single calls become systems: multi-stage pipelines that ingest, parse, score, and verify. Queues, retries, and idempotency — the difference between a demo and production.
Full automation is a myth — great systems put humans exactly where they belong. Review checkpoints, consensus scoring, evals, and guardrails that keep automation trustworthy.
Scope, build, and deploy a real automation product end to end: a working pipeline with an AI core, a human checkpoint, and a live URL you can put in your portfolio.
$ Sixteen weeks, ending in a deployed automation with an AI core and a human checkpoint. Enrolled students pick up where they left off in the student portal.
Toronto, Canada
View CertificateAnslem demonstrated exceptional technical expertise and professionalism throughout our project. His ability to understand complex requirements and deliver robust solutions exceeded our expectations. A true asset to any team.
Working alongside Anslem was an incredible experience. His deep knowledge of full-stack engineering and AI-powered product development, combined with his collaborative spirit, made him an invaluable team member. He consistently delivered high-quality code and was always willing to help others.
$ ./connect --with anslem
I'm open to new opportunities — AI automation, full stack platforms, or the messy problems in between. Tell me what you're building.