MLOps Engineer

Build the infrastructure and processes that enable machine learning at scale. You'll implement MLOps practices, create feature stores, and monitor models in production.

What You'll Do

Design and implement ML pipelines and model deployment systems

Build and maintain feature stores and data versioning

Implement model monitoring for drift, bias, and performance

Create automated retraining and model update workflows

Set up experiment tracking and model registry systems

Develop A/B testing frameworks for ML models

Collaborate with data scientists on productionizing models

What You'll Bring

3+ years of MLOps or ML infrastructure experience

Strong programming skills in Python and SQL

Experience with ML platforms (Kubeflow, MLflow, SageMaker)

Knowledge of containerization and orchestration (Docker, Kubernetes)

Understanding of data engineering and pipeline tools

Experience with monitoring and observability tools

Familiarity with cloud platforms and ML services

Nice to Have

Experience with feature engineering and selection

Knowledge of model explainability and interpretability

Familiarity with streaming data processing

Understanding of federated learning or edge ML

What You'd Build

These aren't hypothetical projects, they're live products you can try before your first interview.

AI Call Center & AI Receptionist, Voice AI that answers business calls in 11 languages and logs every conversation.

AI Ad Intelligence Platform, Creative analytics, social automation and scraping unified under one AI layer.

Why Join StackBinary™?

Flexible working hours

Remote-friendly culture

Learning & development budget

High-ownership projects

Pragmatic engineering culture

Work with cutting-edge tech

Ready to Apply?

Join our team of builders who love shipping quality software.

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Questions about this role?

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