MLOps Engineer

• Readying and deploying machine learning models in a production environment, ensuring scalability, reliability, and performance.
• Implementing and maintaining end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, and deployment.
• Optimizing and fine-tuning machine learning models to improve their accuracy, robustness, and efficiency.
• Collaborating with cross-functional teams to integrate machine learning models into production systems and automate workflows.
• Monitoring model performance and implementing strategies for continuous improvement, including retraining, feature updates, and model versioning.
• Implementing and maintaining CI/CD processes for efficient model development, testing, and deployment.
• Ensuring data quality and security throughout the machine learning lifecycle, including data privacy and compliance with relevant regulations.
• Optimizing infrastructure and processes for cost efficiency, scalability, and reliability.
• Staying up to date with industry best practices and emerging technologies in MLOps and contributing to the development of internal standards and frameworks.

• Bachelor's or higher degree in Computer Science, Engineering, or a related field from an accredited college or university.
• At least 5 years of experience in a relevant field
• Proven experience in developing and deploying machine learning models with particular emphasis on MLOps, operationalizing and maintaining models in production.
• Experience with Google Cloud Platform (GCP) (preferred) or similar cloud platform and its machine learning services.
• Experience with Google Cloud Platform (GCP) services and tools, such as Compute Engine, Cloud Storage, BigQuery, BQML, Vertex AI, Pub/Sub, and Dataflow.
• Familiarity with containerization technologies such as Docker and orchestration frameworks like Kubernetes.
• Strong problem-solving and troubleshooting skills, with the ability to address complex issues in a timely manner.
• Excellent communication skills to effectively convey complex technical concepts to non-technical stakeholders.
• Knowledge of continuous monitoring and observability techniques for machine learning systems.
• Understanding of software development principles and practices, including version control (e.g., Git) and software testing.
• Familiarity with DevOps practices and tools for continuous integration, deployment, and monitoring.
• Understanding of statistics, data structures, and algorithms.
• Familiarity with micro services architecture and serverless computing.
• Understanding of networking and security principles in the context of machine learning systems.
• Desired domain experience including:
  • Experience working in the oil and gas industry, with a focus on equipment health management.
  • Familiarity with condition monitoring techniques and technologies used in the oil and gas sector.
  • Knowledge of predictive maintenance methodologies and tools for optimizing equipment performance and reducing downtime.
  • Experience with data analysis and modeling for equipment health prediction and failure detection.
  • Knowledge of data-driven approaches for optimizing equipment maintenance schedules and reducing operational costs.
  • Understanding of the challenges and complexities associated with managing equipment health in the oil and gas industry, including remote and offshore operations.

• Nice to have, but not required:
  • Strong programming skills in languages such as Python with experience in relevant libraries and frameworks (e.g., TensorFlow, scikit-learn).
  • Proficiency in data manipulation, analysis, and visualization using tools such as pandas, NumPy, and Matplotlib.

Last updated on Oct 11, 2023