Senior ML + DevOps Engineer - Advertising (Mexico)

About Truelogic

At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals.

Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference.

By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future.

Our Client

A global digital transformation network that blends creativity and engineering at scale. With teams spanning design, development, and AI innovation, they help major enterprises navigate changing consumer behaviors, emerging technologies, and the future of digital experiences. Their work pushes the boundaries of storytelling, publishing, and customer engagement across industries.

Job Summary

We are seeking a Senior ML+DevOps Engineer to architect and scale AI / ML infrastructure for next-generation digital experiences. This role is central to building robust, production-grade MLOps pipelines, ensuring seamless deployment, monitoring, and optimization of machine learning models. The ideal candidate brings deep expertise in Python, containerization, and cloud platforms, with hands‑on experience delivering end‑to‑end MLOps solutions. You will collaborate closely with data scientists, engineers, and product teams to power AI‑driven publishing technologies, content automation, and intelligent pipelines.

Responsibilities

• Design and implement end-to-end MLOps pipelines for training, deployment, and inference.
• Build and maintain Python-based APIs (FastAPI) to support ML workflows and data ingestion.
• Configure and manage cloud infrastructure (AWS, GCP, Azure) to support AI / ML workloads.
• Automate deployment, scaling, and monitoring of models using Docker and CI / CD pipelines.
• Implement observability, logging, and alerting with tools like CloudWatch, Prometheus, Grafana, and GCP logging.
• Collaborate with cross-functional teams to define requirements and deliver scalable MLOps solutions.
• Optimize model performance and system reliability while troubleshooting production issues.
• Apply infrastructure-as-code practices (Terraform) for reproducible and maintainable deployments.
• Stay ahead of industry best practices in MLOps, cloud-native engineering, and AI / ML integration.

Qualifications and Job Requirements

What We Offer

Why You’ll Like Working Here

Apply now!

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