MLOps Engineer

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

Join us in building the backbone of production-grade AI systems. As an MLOps Engineer, you will design, deploy, and maintain scalable machine learning infrastructure that powers real-world applications.

You will work at the intersection of machine learning, software engineering, and DevOps—ensuring models move seamlessly from experimentation to reliable production systems. This role is ideal for engineers who enjoy solving complex infrastructure challenges and enabling ML teams to move faster.

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.

Tech Stack & Tools

• Programming: Python, Bash
• ML Tools: MLflow, Weights & Biases, Kubeflow
• Cloud Platforms: AWS (SageMaker, S3, EC2), GCP (Vertex AI), Azure ML
• Orchestration: Airflow, Prefect
• Containerization: Docker, Kubernetes
• Data Tools: SQL, Spark, Kafka (streaming pipelines)
• CI/CD: GitHub Actions, Jenkins, GitLab CI
• Monitoring: Prometheus, Grafana, ELK Stack

Requirements

Key Focus: Build reliable, scalable, and automated ML systems

Required Skills

• 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).

Valuable Experience (Nice to Have)

• Experience with real‑time ML systems or streaming pipelines.
• Familiarity with LLM deployment and inference optimization.
• Knowledge of feature stores and model registries.
• Exposure to distributed systems and large-scale data processing.
• Understanding of monitoring, logging, and observability systems.