Machine Learning Engineer
Rivago Infotech Inc
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Concord, CA
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Mid-Senior level
Posted March 28, 2026
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Responsibilities
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., 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
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
Not Met Priorities
What still needs stronger evidence
Requirements
- 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.
- 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)
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
Preferred Skills
- Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
Education
- (Not required) – 10+ Years of professional experience in Software Engineering & 3+ Years in AIML,
Role : MLOPS Engineer Location : Concord, CA (100% Onsite) 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. 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., 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
Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
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. 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., 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
Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment