Devops Engineer with MLOPS

Role : Devops Engineer with MLOPS
Location : Scottsdale AZ
Rate : $65/hr.
"Must be legally authorized to work in US without need for employer sponsorship now or at any time in the future.”


Model developer with strong DevOps experience JD:
Model developer with strong DevOps experience in machine learning model development and deployment. The role combines hands-on ML engineering with DevOps practices to build, deploy, monitor, and operate scalable ML solutions in environments.

Required Skills & Experience

• 4+ years of hands on experience in machine learning development, deployment, and production support
• Strong programming skills in Python (or equivalent language)
• Proven experience with ML frameworks such as TensorFlow, PyTorch, or Scikit learn
• Experience applying advanced ML algorithms and evaluating model performance in production
• Hands on experience working with Spark for large scale data processing
• Solid understanding of DevOps principles and practices, including:
  • Containerization and orchestration (Docker, Kubernetes)
  • CI/CD pipelines
  • Infrastructure automation
• Proficiency in Ansible and Python for automation tasks
• Experience with observability and monitoring tools such as Prometheus, Grafana, ELK Stack, and OpenTelemetry

Key Responsibilities

• Design, develop, and maintain end to end data science pipelines and ML workflows using Python, R, or similar languages
• Build, test, optimize, and deploy machine learning models, with a focus on fraud detection use cases
• Apply a range of machine learning algorithms and techniques, with a deep understanding of model parameters and performance tuning
• Work with large scale (terabyte level) datasets using Spark and distributed processing frameworks
• Develop and optimize SQL queries for data extraction, transformation, and analysis
• Automate operational workflows, monitoring, and observability across the technology stack
• Implement CI/CD pipelines for ML model training, validation, and deployment
• Collaborate with cross functional teams to ensure scalable, reliable, and secure ML production systems