MLOps Engineer
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Job Location:
Concord, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
4 days ago
Vacancies:
1 Vacancy
Job Summary
- Job Title: ML Ops Engineer
- Location: Concord CA
- Type of Hire: C2C
- Cleint Round Inperson (Lets Target only locals and willing to go for inperson interview @ customer location)
Overview
Tachyon Cortex Machine Learning AI team seeking a ML Ops Engineer 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. 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
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.
Tachyon Cortex Machine Learning AI team seeking a ML Ops Engineer 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. 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
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.
