Machine Learning Ops Engineer

Technical Competencies & Requirements

• Bachelor's degree in Computer Science, Engineering, or related fields OR equivalent experience in software development and ML engineering
• 3+ years of industry experience Kubernetes, PyTorch
• Advanced Python programming skills: Proficiency with data science libraries (numpy, pandas) Experience with visualization tools Ability to write modular, robust, and tested Python code Strong debugging skills for complex ML systems
• Proficiency with data science libraries (numpy, pandas)
• Experience with visualization tools
• Ability to write modular, robust, and tested Python code
• Strong debugging skills for complex ML systems
• Deep learning experience: Implementation of neural network models and training workflows Understanding of model architecture selection Knowledge of model evaluation techniques
• Implementation of neural network models and training workflows
• Understanding of model architecture selection
• Knowledge of model evaluation techniques
• MLOps expertise: Creating experiment tracking systems Building model registries and versioning systems Implementing model deployment pipelines Setting up monitoring for model performance
• Creating experiment tracking systems
• Building model registries and versioning systems
• Implementing model deployment pipelines
• Setting up monitoring for model performance
• Data engineering capabilities: Experience with SQL and database principles Familiarity with database frameworks Ability to create data processing pipelines Experience handling common mining data formats and transformations
• Experience with SQL and database principles
• Familiarity with database frameworks
• Ability to create data processing pipelines
• Experience handling common mining data formats and transformations
• Infrastructure management: Experience with cloud services (AWS/Azure) Understanding of containerization (Docker or Singularity) Knowledge of compute resources for ML
• Experience with cloud services (AWS/Azure)
• Understanding of containerization (Docker or Singularity)
• Knowledge of compute resources for ML
• Testing and quality assurance: Implementing automated tests for ML systems Creating QA/QC systems for model predictions Designing validation steps for data inputs/outputs
• Implementing automated tests for ML systems
• Creating QA/QC systems for model predictions
• Designing validation steps for data inputs/outputs
• Ability to write efficient software following best practices
• Proven ability to thrive in startup environments with low structure and high autonomy
• Strong technical communication skills and ability to collaborate in a remote team setting
• Experience working with machine learning in computer vision, NLP, recommender systems, or scientific applications
• Strong background in probability, machine learning, and data science
• Strong experience with data analysis/processing libraries such as pandas and numpy
• Excellent communication skills for both technical and non-technical audiences
• Self-learner and motivated to pick up new skills

Nice to Have

About Stratum