ML Ops Senior Engineer
Compunnel, Inc., California, MO, United States
The ML Ops Senior Engineer will support the full lifecycle of machine learning solutions, from model development and deployment to operational monitoring and governance.
This role requires deep experience in software engineering, machine learning operationalization, CI/CD automation, and cloud-native ML platform engineering.
The ideal candidate will collaborate closely with predictive AI, data engineering, and software engineering teams to build scalable, automated, and reliable ML pipelines and production systems.
Key Responsibilities
Develop and maintain end-to-end ML pipelines using tools such as MLflow, Kubeflow, or Vertex AI.
Automate model training, testing, deployment, monitoring, and retraining across cloud environments (GCP, AWS, Azure).
Implement CI/CD workflows for ML lifecycle management, including model versioning, promotion, and rollback.
Build mechanisms for automated model governance, documentation, explainability, and compliance.
Monitor production model performance using observability tools and frameworks.
Establish alerting and diagnostic workflows for drift, degradation, and anomalies.
Support enterprise model governance processes such as MRM, model documentation, traceability, and audit readiness.
Platform & Engineering Collaboration
Collaborate with engineering teams to provision containerized environments (Docker, Kubernetes).
Support low-latency model scoring and API-driven inference services.
Integrate ML systems with data pipelines, ETL workflows, and distributed computing frameworks (Airflow, Spark).
Leverage AutoML solutions (Vertex AI AutoML, H2O Driverless AI) for rapid experimentation and deployment.
Required Qualifications
10+ years of professional software engineering experience.
3+ years of hands-on experience in AI/ML engineering or ML Ops.
Strong proficiency in Java, Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
Deep experience with cloud platforms and containerization technologies (Docker, Kubernetes).
Familiarity with data engineering tools such as Airflow or Spark, and ML Ops frameworks.
Solid understanding of software engineering best practices, CI/CD, DevOps, and automation.
Ability to communicate complex technical concepts to non-technical audiences and collaborate effectively with cross-functional teams.
Preferred Skills
Experience building model governance frameworks.
Exposure to scalable microservices, REST APIs, and event-driven architectures for model deployment.
Familiarity with security, compliance, and responsible AI practices.
#J-18808-Ljbffr
This role requires deep experience in software engineering, machine learning operationalization, CI/CD automation, and cloud-native ML platform engineering.
The ideal candidate will collaborate closely with predictive AI, data engineering, and software engineering teams to build scalable, automated, and reliable ML pipelines and production systems.
Key Responsibilities
Develop and maintain end-to-end ML pipelines using tools such as MLflow, Kubeflow, or Vertex AI.
Automate model training, testing, deployment, monitoring, and retraining across cloud environments (GCP, AWS, Azure).
Implement CI/CD workflows for ML lifecycle management, including model versioning, promotion, and rollback.
Build mechanisms for automated model governance, documentation, explainability, and compliance.
Monitor production model performance using observability tools and frameworks.
Establish alerting and diagnostic workflows for drift, degradation, and anomalies.
Support enterprise model governance processes such as MRM, model documentation, traceability, and audit readiness.
Platform & Engineering Collaboration
Collaborate with engineering teams to provision containerized environments (Docker, Kubernetes).
Support low-latency model scoring and API-driven inference services.
Integrate ML systems with data pipelines, ETL workflows, and distributed computing frameworks (Airflow, Spark).
Leverage AutoML solutions (Vertex AI AutoML, H2O Driverless AI) for rapid experimentation and deployment.
Required Qualifications
10+ years of professional software engineering experience.
3+ years of hands-on experience in AI/ML engineering or ML Ops.
Strong proficiency in Java, Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
Deep experience with cloud platforms and containerization technologies (Docker, Kubernetes).
Familiarity with data engineering tools such as Airflow or Spark, and ML Ops frameworks.
Solid understanding of software engineering best practices, CI/CD, DevOps, and automation.
Ability to communicate complex technical concepts to non-technical audiences and collaborate effectively with cross-functional teams.
Preferred Skills
Experience building model governance frameworks.
Exposure to scalable microservices, REST APIs, and event-driven architectures for model deployment.
Familiarity with security, compliance, and responsible AI practices.
#J-18808-Ljbffr