Sign upLog in

Sr MLop engineer

Syntricate Technologies

San Leandro, CA

Apply
JOB DETAILS
JOB TYPE
Full-time
SKILLS
Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Automation, Cloud Computing, Communication Skills, Continuous Deployment/Delivery, Continuous Integration, Customer/Client Research, Data Modeling, Data Science, DevOps, Docker, Documentation, Documentation Models, Environmental Monitoring, GCP (Good Clinical Practices), Machine Learning, Maintain Compliance, Microsoft Windows Azure, Performance Analysis, Performance Modeling, Predictive Modeling, Python Programming/Scripting Language, SQL (Structured Query Language), Scalable System Development, Software Engineering, Structured Data, Unstructured Data
LOCATION
San Leandro, CA
POSTED
30+ days ago
Job Summary: 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.

About the Company

S

Syntricate Technologies