Machine Learning Engineer

La Moda

full-time

Posted on: 1/12/2026

Location Type: Remote

Location: Brazil

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About the role

ML Systems Architecture: Develop and implement scalable architectures for model training and inference.
Data and ML Pipelines: Build and optimize CI/CD pipelines for Machine Learning (MLOps), automating everything from data processing to deployment and monitoring.
Performance Optimization: Ensure low latency and high availability of models served via APIs or batch processing.
Cross-functional Collaboration: Work closely with Data Scientists to translate business requirements into technical specifications and ensure reproducibility of experiments.
Technical Mentorship: Mentor junior team members, promoting coding best practices (Clean Code), testing, and technical documentation.
Infrastructure Management: Manage and optimize the cost of compute resources (GPUs/TPUs) and managed services on GCP.

Solid Experience: Proven track record of deploying ML models to production (senior level).
GCP Expertise: Deep experience with the Google Cloud ecosystem, especially:
- Vertex AI (Pipelines, Model Registry, Feature Store, Endpoints).
- BigQuery & BigQuery ML.
- Dataflow or Dataproc for large-scale data processing.
- Cloud Storage and Artifact Registry.
Languages and Frameworks: Proficient in Python and experienced with libraries such as TensorFlow, PyTorch, or scikit-learn.
Software Engineering: Experience with Docker, Kubernetes (GKE), and CI/CD tools (GitHub Actions, GitLab CI, or Cloud Build).
Databases: Advanced SQL skills and familiarity with NoSQL databases.
MLOps: Strong knowledge of model monitoring (drift detection) and versioning for data (DVC) and models (MLflow).

Tip: use these terms in your resume and cover letter to boost ATS matches.

Machine LearningMLOpsPythonTensorFlowPyTorchscikit-learnSQLNoSQLmodel monitoringversioning

cross-functional collaborationtechnical mentorshipcoding best practicescommunication