Machine Learning Engineer-Technical Lead

Clarios; Monterrey; 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 in Monterrey. You will architect and implement production-grade ML solutions powering our industrial IoT platform, guiding engineers and scientists to transform sensor data into actionable intelligence.

You will own end-to-end ML projects, mentor a diverse team, and drive cross-functional collaboration with product managers to align technical outcomes with business goals.

Formación

• 5+ years in ML and software engineering.
• Experience leading technical teams in production.
• Strong Python and ML frameworks proficiency.
• Experience with time-series data.
• Excellent cross-functional collaboration.

Responsabilidades

• Architect and deploy ML solutions for predictive maintenance and forecasting.
• Build robust data pipelines and real-time inference systems.
• Lead end-to-end ML project delivery from ideation to deployment.
• Mentor team and enforce best practices.

Conocimientos

Team leadership

ML foundations

Production engineering

Python

Cloud platforms

Edge & cloud

Educación

Bachelor’s degree in CS/EE/Statistics

Advanced degree preferred

Herramientas

PyTorch

TensorFlow

Scikit-learn

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)

Required

• 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.

About Clarios:

Clarios is the global leader in advanced, low-voltage battery technologies for mobility. Our batteries and smart solutions power nearly every type of vehicle and are found in 1 of 3 cars on the road today. With around 18,000 employees in over 100 countries, we bring deep expertise to our Aftermarket and OEM partners, and reliability, safety and comfort to everyday lives. We answer to the planet with a rigorous sustainability focus – advancing best-in-class sustainability practices and advocating for them across our industry. We work to ensure 100% of our products sold are recyclable, and we recycle 8,000 batteries an hour in our network. You can find more information here (PDF).

To All Recruitment Agencies : Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.

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].