Open to opportunities

Jefferson Barboza

@jeffersonbarboza

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Senior AI/ML engineer specializing in production-grade inference systems and cloud-native AI infrastructure.

Brazil

Message

What I'm looking for

I seek senior engineering roles where I can design and operate production AI infrastructure, lead MLOps practices, improve observability, and mentor teams in high-availability, cost-efficient systems.

I am a Senior AI/ML Engineer with 9+ years building and scaling production-grade AI platforms across Europe and LATAM, focused on resilient inference systems, distributed ML pipelines, and cost-efficient cloud-native infrastructure. I have a proven track record of turning experimental models into observable, reliable production services, optimizing latency, and implementing MLOps practices like CI/CD, blue/green deployments, monitoring, and runbooks.

My hands-on experience includes designing event-driven microservices, vector search systems, model lifecycle automation, and mentoring engineers to standardize schemas and API contracts. I deliver end-to-end AI features—from UI and API integration to deployment on AWS/GCP—and drive observability, drift detection, and performance tuning to reduce MTTR and maintain high-traffic reliability.

Experience

Work history, roles, and key accomplishments

Senior AI Engineer

Ubidots

Aug 2022 - Feb 2026 (3 years 6 months)

Owned end-to-end delivery of AI features, migrated legacy pipelines to vector-backed services, and implemented MLOps CI/CD on AWS to reduce inference p95 and improve deployment reliability.

Python NestJS AWS Sagemaker Docker Kubernetes Celery Prometheus

Mid-Senior AI Engineer

Remerge

Apr 2020 - Jul 2022 (2 years 3 months)

Built AI authentication and reporting APIs, offloaded batch inference to workers, and established MLOps pipelines with GitLab CI and SageMaker to enable safe model validation and deployment.

Python NestJS RabbitMQ Redis Docker Gitlab CI Sagemaker

Junior AI Developer

Brainhub

Jul 2017 - Mar 2020 (2 years 8 months)

Developed an internal ML model inventory and compliance dashboard, automated nightly reconciliation and drift detection jobs, and improved model serving tests and caching to increase reliability.