Machine Learning Engineer-Technical Lead

Clarios; San Pedro Garza García; Presencial; MXN 900.000 - 1.200.000; Jornada completa

Descripción de la vacante

Clarios is seeking a seasoned Machine Learning Engineer to join the Connected Services AIML team as a Technical Lead. You will architect and implement algorithmic solutions for our industrial IoT platform while guiding a team to deliver impactful systems.

Successful candidates will have strong leadership capabilities and a solid background in ML, with a bachelor's degree in a relevant field and over 5 years of experience in machine learning and software engineering.

Formación

• 5+ years of experience in machine learning and software engineering.
• Proven experience leading technical teams or projects in a production environment.
• Solid understanding of core ML/AI algorithms: supervised and unsupervised learning.

Responsabilidades

• Architect & deploy ML solutions for predictive maintenance and anomaly detection.
• Develop robust data pipelines and real-time inference systems.
• Own the end-to-end delivery of ML projects.

Conocimientos

Machine Learning

Leadership

Python

Cloud Platforms

Data Pipelines

Educación

Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or related field

Herramientas

PyTorch

TensorFlow

Scikit-learn

SQL

MLOps tools (MLflow, Airflow, Docker, Kubernetes)

Descripción del empleo

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, and edge + cloud deployment.
• Mentorship and the ability to raise the bar across an engineering team.
• Excellent communication and cross‑functional collaboration.

What You Bring (Qualifications)

• Bachelor’s degree in Computer Science, Electrical Engineering, Statistics, or a related field.
• 5+ years of experience in machine learning and software engineering.
• Proven experience leading technical teams or projects in a production environment.
• Solid understanding of core ML/AI algorithms: supervised and unsupervised learning, classification, regression, clustering, and deep learning techniques.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit‑learn), SQL, and cloud platforms.
• Experience with time‑series data.
• Excellent communication and cross‑functional collaboration skills.

Preferred

• Advanced degree in Computer Science, Electrical Engineering, Statistics, or a related field.
• Experience in industrial sectors.
• Knowledge of MLOps tools (MLflow, Airflow, Docker, Kubernetes).
• Experience with one or more of signal processing, edge computing, and physics‑informed ML models.

Equal Employment Opportunity

Clarios, LLC is an equal employment opportunity and affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, status as a qualified individual with a disability, or any other characteristic protected by law. For more information, please view EEO is the Law, EEO is the Law (supplement), and Pay Transparency Non‑discrimination. If you are an individual with a disability and you require an accommodation during the application process, please email [e-mail].