What are the responsibilities and job description for the MLOps Engineer position at K-Tek Resourcing LLC?
Job Details
Key Responsibilities
Required Qualifications
- Design, develop, and maintain end-to-end ML pipelines using tools such as MLflow, Kubeflow, Vertex AI.
- Automate model training, validation, deployment, monitoring, and retraining in cloud environments (Google Cloud Platform, Azure, AWS).
- Implement and manage CI/CD workflows for the ML lifecycle, including:
- Model versioning
- Performance monitoring
- Automated retraining
- Monitor model health and performance using observability tools; ensure compliance with model governance frameworks (MRM, documentation, explainability).
- Collaborate with engineering teams to:
- Provision containerized environments (Docker, Kubernetes)
- Support low-latency model scoring APIs
- Leverage AutoML platforms such as:
- Vertex AI AutoML
- H2O Driverless AI
for rapid model development, deployment, and documentation automation.
- Work closely with cross-functional stakeholders to translate business problems into scalable ML solutions.
Required Qualifications
- 10 years of professional experience in Software Engineering.
- 3 years of hands-on experience in AI/ML and MLOps.
- Strong experience in both ML Engineering and Data Science concepts.
- Proficiency in:
- Python & Java
- SQL
- ML libraries: scikit-learn, XGBoost, TensorFlow, PyTorch
- Strong hands-on experience with Spark (PySpark preferred) for large-scale data processing.
- Experience with cloud platforms, with strong preference for:
- Google Cloud Platform (Vertex AI, AutoML)
- Microsoft Azure
- Expertise in:
- Docker
- Kubernetes
- ML pipeline orchestration
- Experience with data engineering and workflow tools such as:
- Apache Airflow
- Spark
- Solid understanding of DevOps, CI/CD, and software engineering best practices.
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