Predictive AI team seeking ML Ops Engineers to drive the full lifecycle of machine learning
solutions.
Key Responsibilities
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., Google Cloud Platform, 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
Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
Qualifications
10+ Years of professional experience in Software Engineering & 3+ Years in AIML,
Machine Learning Model Operations.
Strong proficiency in Java and 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.
Location: Bay Area
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