// AI Engineer & Tech Lead

Kevin Bayter

I build AI systems that survive production.

AI Engineer and tech lead. I modernize critical payments and high-traffic platforms at Mercado Libre and WOM Colombia, and design agents with deterministic guardrails where failure is not an option.

View missions Contact

scroll down

years building production systems

teams led as tech lead

100%

of the team ships with my platform

24/7

payments & high traffic in production

01 Toolbelt

periodic table · 20 elements

What I build and run production systems with

01 streaming

Kafka

02 framework

Spring

03 infra

Kubernetes

04 cloud

AWS

05 data

Redis

06 testing

Playwright

07 infra

Terraform

08 language

09 language

TypeScript

10 language

Java

11 language

Kotlin

12 runtime

Node.js

13 frontend

React

14 frontend

Next.js

15 data

GraphQL

16 data

PostgreSQL

17 infra

Docker

18 observab.

Grafana

19 observab.

Datadog

20 ci/cd

GitLab

periodic table of tools 20 elements · all battle-tested in production

02 Onboard systems

6 operational modules · nominal status

Skills

From agent design with deterministic guardrails to resilient payment platforms: depth across the whole stack.

Module 01

AI agents

Production agentic systems with deterministic guardrails.

Agents in productionLLMs (OpenAI-compatible)Tool orchestrationStructured extractionDeterministic guardrailsGolden tests

Module 02

Backend & Payments

High-traffic reactive and transactional flows.

Spring BootSpring WebFluxKafkaRedisGraphQLREST / μservicesNode.jsNext.js

Module 03

Languages

Polyglot with focus on JVM, Go, and TypeScript.

JavaGoTypeScriptJavaScriptKotlinSQL

Module 04

Cloud & DevOps

Infrastructure as code, on-premise and multi-cloud.

Kubernetes (on-prem + EKS)TerraformAWSGCPAzureGitLab CI / GH ActionsAPISIX

Module 05

Observability & Security

Fast diagnosis and hardened applications.

GrafanaDatadogNew RelicOpsgenieOWASPBurp Suite

Module 06

Testing & Data

Automated quality from UI to database.

PlaywrightSeleniumJestJUnitVitestPostgreSQLOracleMySQL

03 Orbital station

⎈ kubernetes on-premise · everything runs in production

Infrastructure

I built the team's on-premise platform from scratch: a declaratively managed Kubernetes cluster with an API gateway, automatic TLS, highly available data stores, and full observability.

⎈ kubernetes on-premise

users api-gateway · auth · rate-limit · tls

service-aservice-bservice-c

data & security

postgres · HA · operatorredis · cache · queuesregistry · signed imagescert-manager · tls

observability · scrape

prometheus grafana alerts

ephemeral namespaces — one per feature · created and destroyed automatically

API gateway with authentication, rate limiting, and automatic TLS issued by cert-manager.
Highly available Postgres with an operator, Redis for cache and queues, and a private registry of signed images.
Prometheus and Grafana with metrics, dashboards, and alerts for every service in the cluster.

04 Launch control

4 sequences · full audit trail

The deployment platform

I designed and built the internal platform the whole team deploys with: from a push to production with ephemeral environments, managed secrets, a chat bot, and an audit trail for every step.

Sequence 01

End-to-end deploys

A push triggers the whole pipeline: build with tests, signed image in the private registry, deploy to an isolated ephemeral namespace, automated smoke tests, and promotion to production.

git push→build ci→registry→ephemeral ns→smoke tests→production

Every feature gets its own ephemeral environment with its own URL, created and destroyed automatically.
Nothing reaches production without passing the smoke tests in the ephemeral environment.
One-click rollback to the previous version, with full history.

Sequence 02

Secrets management

Secrets live encrypted in a central store with role-based access and are injected into pods at deploy time. They never touch a repository and are masked in every log.

encrypted store→runtime injection→pod · env · volumes→logs ••••••••·git ✗ never in the repo

Credential rotation without touching code or redeploying by hand.
Each service only sees the secrets it owns.
Audit trail of who accessed what and when.

Sequence 03

The deploy bot

A bot embedded in the team chat runs deploys, rollbacks, and status checks with one command. It validates permissions against the platform and replies with the rollout result in real time.

team chat
❯ /deploy api v1.2
✓ deployed · 2 replicas ready

Deploying from chat removes friction: no consoles or dashboards to open.
Role-based permissions: everyone can only operate their own services.
Proactively reports deployment health incidents.

Sequence 04

Internal architecture

The web UI and the bot talk to a central API; heavy jobs (builds, rollouts, smoke tests) run on async workers coordinated with Redis queues, deploying to both the on-premise cluster and cloud EKS.

web ui · bot→api core→workers · redis queues→k8s on-prem+eks cloud

Multi-target: the same flow deploys to on-premise and to the cloud.
Async workers: the UI never blocks waiting for a rollout.
Every event lands in an immutable audit log.

05 Build protocol

spec-driven · agents in parallel

How I built it

Spec-driven development with AI agent orchestration: every feature starts as an executable spec that several agents implement in parallel, with deterministic quality gates before anything reaches production.

spec.md · executable → orchestrator → agent-1 ∥ agent-2 ∥ agent-3 → quality gates · tests · lint · human → merge→deploy

The spec defines the goal and acceptance criteria before any code is written.
Several AI agents work in parallel, each owning one part of the spec.
Nothing merges without passing tests, architecture linting, and human review.

06 Quality control

from commit to deploy

Quality & delivery

How I test and ship software: Playwright E2E against the real build and CI/CD pipelines with parallel quality, from commit to deploy.

Sequence 05

E2E with Playwright

Smoke E2E tests run against the real production build served locally, with the backend mocked inside the browser itself: fast, deterministic, and independent of external environments. The full critical flow is verified in a real browser before every merge.

production build→playwright · chromium→mocked backend→ci green

A separate layer from unit tests: it exercises the packaged build, not the source code.
Traces and screenshots are kept only on failure — light artifacts, full debugging.
The same tests gate the merge in CI: if the critical flow breaks, nothing lands.

Sequence 06

CI/CD pipelines

Frontend and backend share the same philosophy: quality in parallel (lint, unit, e2e, static analysis, and a coverage gate), unprivileged image builds, and deployment through the shared platform template — which also provides the rollback job.

install · per-branch cache→lint ∥ unit ∥ e2e ∥ coverage→oci image→deploy · rollback

Parallel quality jobs with per-branch caching: feedback in minutes, not hours.
Nothing deploys without passing the full quality stage.
Shared platform template: deploy and rollback are identical across every repository.

07 Mission log

8/8 objectives verified in production

Goals achieved

100% of the dev team ships daily through the platform

E2E end-to-end auditable rollouts, with history and rollback

min an ephemeral environment per feature, ready in minutes with its own URL

TLS automatic certificates on every service in the cluster

24/7 metrics, dashboards, and alerts for every service in production

0 secrets in code repositories — encrypted runtime injection

1→N on-premise kubernetes cluster built from scratch and scaled

2 deployment targets with the same flow: on-premise and cloud

08 Flight log

Journey

My story as a git log: the ── main branch is experience, ╌╌ learn is education.

f4c3a1e · nov 2025 — present feat

Software Engineer Specialist · Tech Lead

WOM Colombia · Bogotá

I lead the modernization of the payments layer and integrations with core business services (APISIX, secrets management, observability), using Redis for caching and idempotency in critical transactional flows.

Built the internal deployment platform adopted by the whole dev team: orchestrates on-premise Kubernetes and AWS/EKS, IaC, secrets, and change traceability across environments.
Standardized software delivery for multiple teams with end-to-end auditable rollouts, reducing friction and manual errors in production.
Drive AI adoption across the engineering cycle: assisted code review, alert analysis, and automation of repetitive ops tasks.
Support the evolution of the BSS and payments ecosystem with focus on resilience, scalability, and operational continuity.

APISIXRedisKubernetesAWS / EKSTerraformPayments

b7d29c4 · 2025 learn

Cloud Computing — AWS & Azure

Universidad de los Andes · Bogotá · certification

Cloud computing certification with AWS and Azure: architecture, managed services, and multi-cloud best practices.

AWSAzure

9e1f6a2 · jan 2022 — sep 2025 feat 3.7 years · 24/7 high traffic

Software Engineer

Mercado Libre · Bogotá

Built backend and front-end solutions with Go, Node.js, TypeScript, and Next.js to automate internal flows and improve the experience of users and ops teams.

Integrated observability and incident tooling (Datadog, New Relic, Grafana, Opsgenie), cutting diagnosis and response times.
Worked on high-traffic services using Redis as distributed cache, session storage, and rate limiting to protect critical APIs.
Collaborated on AI automations for alert analysis and ticket generation, improving team productivity.
Applied security practices across the dev cycle: validations, reviews, and testing for sensitive applications.

GoNode.jsTypeScriptNext.jsRedisDatadogGrafana

d5a80cb · 2022 — 2026 learn · in progress

Systems Engineering (BSc)

Fundación Universitaria del Areandina · Bogotá

Systems engineering degree pursued in parallel with my professional career, with real software projects.

Ticket management system for airport operations.
Gym membership management app with payments and tracking.
Dynamic library using the ChatGPT API to automate SEO generation for websites.

JavaGoPythonTypeScriptGoogle Cloud

7c4e912 · 2021 — 2022 learn · 800+ hours

Fullstack Developer Bootcamp

Henry · Remote

Intensive full-stack bootcamp with modern technologies and agile methodologies. Multiple projects with React, Node.js, and PostgreSQL; REST APIs and agile teamwork.

ReactNode.jsPostgreSQLTypeScript

31bfa60 · 2017 · ignition feat

IT Support Specialist

FACTURE S.A.S. · Cartagena

Software and hardware technical support, partner-company tickets, and remote-connection solutions. Reduced downtime through proactive maintenance and monitoring.

LinuxmacOSNetworking

09 Featured missions

Projects

Real production systems that demonstrate architecture, applied AI, and engineering judgment.

Mission 01 · Creator Open source

TuRenta AI

turenta.tax ↗

Open-source platform that automates income tax filing in Colombia (form 210): AI agents extract data from tax reports and certificates, interview the user, and a 100% deterministic tax engine computes the return — in a domain where a hallucination is a legal problem.

Hexagonal architecture with boundaries enforced by ESLint and architecture tests.
Golden test that reproduces a real tax return line by line as an accuracy guarantee.
Async workers with Redis for queues, rate limiting, and sessions.

TypeScriptNext.jsTurborepo / pnpmPostgreSQLRedisPlaywrightDockerGitHub Actions

agent telemetry

01 · playwright → dian.gov.co ✓

Connects to the DIAN (tax authority) and auto-downloads reports, certificates, and filings.

02 · llm.extract(docs) ✓

Agents extract structured data with a double read of every value.

03 · human.confirm(values) ✓

Guided interview: every extracted value is confirmed before use.

04 · tax_engine.compute(210) ✓

100% deterministic engine with rules that cite the law — zero hallucinations.

» system principle: AI reads, code calculates

Mission 02 · WOM · internal

Deployment platform

Internal platform adopted by the entire dev team: orchestrates deployments to on-premise Kubernetes and AWS/EKS, manages infrastructure as code, secrets, and change traceability across environments, with end-to-end auditable rollouts.

KubernetesAWS / EKSTerraformGitLab CIIaC

Mission 03 · full-stack

Feedback360

Complete professional feedback platform: teams with visibility levels (public, private, corporate), real-time notifications, Google login and OTP auth, goal management, and an admin dashboard.

ReactNode.jsExpressMongoDB

languages es_CO · Spanish native pt_BR · Portuguese advanced en_US · English professional cert ✓ Cloud Computing AWS & Azure · Uniandes 2025

10 · Incoming transmission

Let's talk

Looking for an AI Engineer who can also run payments, Kubernetes, and observability in production? Reach out.

Email kevin@bayterx.com LinkedIn linkedin.com/in/bayterx GitHub github.com/kevinbayter

open to new challenges · 100% remote · any time zone

New transmission▷ direct channel