Machine Learning Engineer-Technical Lead (San Pedro Garza García)

Machine Learning Engineer-Technical Lead (San Pedro Garza García)

06 ago
|
Clarios
|
San Pedro Garza García

06 ago

Clarios

San Pedro Garza García

The Role
We are looking for a seasoned Machine Learning Engineer with strong leadership capabilities to join the Connected Services AIML team as a Technical Lead. You will architect and implement the algorithmic solutions that power our industrial IoT platform, while guiding a team of engineers and scientists to deliver high-impact, production-grade systems — turning large-scale sensor data into intelligence that keeps connected assets running.
What You’ll Do (Impact Areas)
- Architect & deploy ML solutions: design and deploy models for predictive maintenance, anomaly detection, asset optimization, and time-series forecasting using large-scale sensor data from connected devices.
- Build production infrastructure: develop robust data pipelines and real-time inference systems integrated with edge and cloud infrastructure.
- Lead technical execution: own the end-to-end delivery of ML projects, from ideation to deployment.
- Mentor & set the standard: guide a team of ML and software engineers; define and enforce best practices in model development, testing, and deployment.




- Partner cross-functionally: work with product managers and domain experts to align technical solutions with business goals.

What Success Looks Like
- Production-grade models for predictive maintenance and anomaly detection deployed and reliably serving real-time inference at scale.
- Robust, well-documented pipelines moving sensor data from edge to cloud with minimal latency and downtime.
- A high-performing ML and software engineering team operating against clear, enforced best practices.
- Technical roadmaps that map directly to business outcomes and are delivered on time, from ideation to deployment.

Core Competencies
- Technical leadership and end-to-end project ownership in a production environment.
- Strong ML/AI foundations: supervised and unsupervised learning, classification, regression, clustering, and deep learning.
- Production engineering mindset: data pipelines, real-time inference,

📌 Machine Learning Engineer-Technical Lead (San Pedro Garza García)
🏢 Clarios
📍 San Pedro Garza García

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