MLOps Engineer — Build Scalable AI Pipelines in Production

Sierracorp; San Francisco (CA); On-site; USD 100,000 - 150,000; Full time

Job summary

Sierracorp in San Francisco is looking for an MLOps Engineer to design and maintain scalable ML infrastructure. Your role will involve creating automated ML systems, monitoring performance, and ensuring robust deployment pipelines. Strong experience in Python and familiarity with cloud platforms like AWS, GCP, or Azure will be crucial. You will work alongside ML engineers and data scientists to enhance production systems. The position welcomes engineers who live to solve complex challenges in machine learning and infrastructure.

Qualifications

• Strong experience in Python and software engineering fundamentals.
• Hands-on experience with MLOps tools and pipeline automation.
• Experience deploying ML models in production environments.
• Familiarity with cloud platforms (AWS/GCP/Azure).
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Understanding of ML lifecycle and model evaluation concepts.
• Experience with CI/CD pipelines and version control (Git).

Responsibilities

• Design, build, and maintain end-to-end ML pipelines.
• Automate model training, validation, and deployment workflows.
• Develop CI/CD pipelines specifically for ML systems.
• Monitor production models for performance, drift, and reliability.
• Manage model versioning, experiment tracking, and reproducibility.
• Collaborate with ML engineers, data scientists, and backend teams.
• Optimize infrastructure for scalability, cost, and performance.
• Ensure best practices in security, governance, and compliance.

Skills

Python

MLOps tools

Cloud platforms (AWS/GCP/Azure)

Docker

Kubernetes

Tools

MLflow

Weights & Biases

Kubeflow

Airflow

Prefect

SQL

Spark

Kafka

GitHub Actions

Jenkins

GitLab CI

Prometheus

Grafana

ELK Stack

Job description

Sierracorp in San Francisco is looking for an MLOps Engineer to design and maintain scalable ML infrastructure. Your role will involve creating automated ML systems, monitoring performance, and ensuring robust deployment pipelines. Strong experience in Python and familiarity with cloud platforms like AWS, GCP, or Azure will be crucial. You will work alongside ML engineers and data scientists to enhance production systems. The position welcomes engineers who live to solve complex challenges in machine learning and infrastructure.