Software Mind

Machine Learning Engineer

Software Mind

full-time

Posted on:

Origin:  • 🇨🇷 Costa Rica

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Job Level

Mid-LevelSenior

Tech Stack

AirflowAmazon RedshiftAWSAzureBigQueryCloudDockerETLGoogle Cloud PlatformKubernetesPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

  • Architect and implement complex dbt pipelines following modern data engineering best practices
  • Design scalable, high-performance data models that power critical business analytics
  • Build and optimize advanced SQL queries across massive datasets
  • Implement robust data testing, quality checks, and monitoring systems
  • Develop and maintain comprehensive documentation for data models and processes
  • Design and implement end-to-end ML pipelines from feature engineering to deployment
  • Operationalize ML models in production environments with monitoring and retraining workflows
  • Collaborate with data scientists to transform research models into scalable production systems
  • Implement feature stores and develop feature engineering pipelines
  • Optimize ML model performance, scalability, and reliability
  • Partner closely with stakeholders to translate business requirements into technical solutions
  • Create advanced analytics solutions that drive high-value business decisions
  • Develop and maintain sophisticated data transformations that support ML initiatives
  • Lead technical design discussions and mentor junior team members
  • Stay current with cutting-edge techniques in analytics engineering and MLOps

Requirements

  • Located in Latam (candidates located in Latam to fill the role)
  • +90% English written and oral (at least B2 level) with excellent communication skills
  • Bachelor's degree in Computer Science, Statistics, Engineering, or a related quantitative field (Master's preferred)
  • 5+ years of experience in analytics engineering, ML engineering, or similar roles
  • Expert-level SQL skills with deep experience in at least one major data warehouse (Snowflake, BigQuery, Redshift)
  • Advanced proficiency with DBT, including complex transformation patterns and best practices
  • Strong Python programming skills with experience building production ML pipelines
  • Hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch) and MLOps tools
  • Experience deploying and monitoring ML models in production environments
  • Proven track record implementing data quality frameworks and testing methodologies
  • Expertise with version control, CI/CD practices, and modern data engineering workflows
  • Exceptional problem-solving abilities and attention to detail
  • Non-negotiable: Strong SQL and Python proficiency
  • Non-negotiable: Production ML model deployment experience
  • Non-negotiable: Data pipeline and ETL/ELT engineering experience
  • Preferred: Master's or PhD in a quantitative field
  • Preferred: Experience architecting both batch and real-time ML pipelines
  • Preferred: Expertise with feature stores (Feast, Tecton, etc.) and feature engineering at scale
  • Preferred: Advanced knowledge of MLOps frameworks and tools (MLflow, Kubeflow, Airflow, etc.)
  • Preferred: Experience with containerization (Docker) and orchestration (Kubernetes)
  • Preferred: Deep expertise in cloud platforms (AWS, GCP, Azure) and their ML/data services
  • Preferred: Experience with real-time analytics systems and streaming architectures
  • Preferred: Background in causal inference, A/B testing, and experimental design
  • Preferred: Knowledge of data governance, data security, and ML model governance principles
  • Preferred: Experience leading technical teams or mentoring junior engineers