ML Ops Senior Engineer

Compunnel, Inc.; California (MO); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading technology firm is looking for an experienced ML Ops Senior Engineer to support the lifecycle of machine learning solutions. Responsibilities include building ML pipelines, automating model deployment, and ensuring model governance. Ideal candidates have over 10 years in software engineering, solid skills in Java and Python, and expertise in cloud platforms like AWS, GCP, or Azure. This role requires collaboration with cross-functional teams to deliver effective ML solutions.

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.
• Deep experience with cloud platforms and containerization technologies.
• Solid understanding of CI/CD, DevOps, and automation.

Responsibilities

• Develop and maintain end-to-end ML pipelines using MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, monitoring, and retraining.
• Implement CI/CD workflows for ML lifecycle management.
• Monitor production model performance using observability tools.
• Collaborate with engineering teams for containerized environments.

Skills

Software engineering

Machine learning operationalization

CI/CD automation

Cloud native ML platform engineering

Java

Python

SQL

ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch)

Docker

Kubernetes

Airflow

Spark

Tools

MLflow

Kubeflow

Vertex AI

Job description

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.