ML Ops Engineer

TechDigital Group; San Leandro (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading tech company in California is seeking an ML Ops Engineer to manage the full lifecycle of machine learning solutions, from model development to deployment. The ideal candidate has strong Python and SQL skills and experience with cloud platforms and containerization. This role offers the chance to work on impactful projects involving predictive modeling and automation of ML pipelines.

Qualifications

• Strong proficiency in Python, SQL, and ML libraries.
• Experience with cloud platforms and containerization.
• Familiarity with data engineering tools and ML Ops frameworks.

Responsibilities

• Develop predictive models using structured/unstructured data.
• Leverage AutoML tools for model development and rapid deployment.
• Automate model training, testing, deployment, and monitoring.

Skills

Python

SQL

Scikit-learn

XGBoost

TensorFlow

PyTorch

Tools

Docker

Kubernetes

MLflow

Kubeflow

Vertex AI

Airflow

Spark

Job description

ML Ops Engineer to drive the full lifecycle of machine learning solutions—from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.

Key Responsibilities

• Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.
• Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment
• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs

Qualifications

• Strong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
• Solid understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.