ML Developer / MLOps Engineer
About the Role
Location
Mexico Querétaro Santiago de Queretaro Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy, S. de de Organization Grid Technologies Business Unit Product Management Full / Part time Full-time Experience Level Mid-level Professional
A Snapshot of Your Day
The day of an ML Developer/MLOps Engineer starts the day by reviewing model performance metrics and identifying any drift in production models. A team meeting follows, where updates on model training and evaluation pipelines are shared. The engineer then works on converting Jupyter notebooks into reproducible training pipelines, ensuring proper version control. After lunch, they package and serve a new machine learning model via Azure ML Endpoints, collaborating with data engineers to manage data and feature pipelines. The day concludes with documenting the integration process and planning for improvements based on stakeholder feedback.
Bridge the gap between data science and production: package models into reliable, secure, and scalable AI applications on Azure, with a focus on automation, observability, and operational excellence.
How You’ll Make an Impact
Build, deploy, and operate AI applications as production-grade microservices on Azure (App Services, Container Apps, AKS).
Develop and maintain robust MLOps CI/CD pipelines for model training, testing, versioning, and deployment.
Package and serve models as containerized, scalable API endpoints (Docker, Azure Container Services, APIM).
Implement observability: monitor application performance, model accuracy, and drift; establish alerting, rollback, and retraining strategies.
Apply software engineering practices to ML workflows: modular design, automated tests, reproducibility, traceability.
Availability to travel and visit projects.
What You Bring
Strong proficiency in Python, with experience in ML frameworks such as PyTorch, scikit-learn, and HuggingFace, along with data libraries like pandas and NumPy. Skilled in API development using FastAPI or Flask for serving models into applications.
Comprehensive understanding of the AI/ML lifecycle, including training, validation, deployment, monitoring, retraining, and scaling of models in production environments.
Hands-on experience in cloud development, ideally with Azure, including containerization using Docker, and implementation of CI/CD pipelines.
Practical experience with LLM-based solutions, including RAG pipelines, vector stores, evaluation loops, and basic frontend integration (feedback UIs, annotation loops).
Familiarity with key MLOps tools, such as MLflow for experiment tracking, feature stores, A/B testing for models, and monitoring frameworks like Prometheus, Grafana, and OpenTelemetry for ML-specific metrics.
Advanced English proficiency, with strong leadership, communication, and adaptability skills; comfortable working in agile, collaborative environments using Git and version control systems.
📌 ML Developer / MLOps Engineer
🏢 Siemens Energy
📍 Santiago Mexquititlán