Job Information
Infoblox Data Engineer II in Tacoma, Washington
Description It's an exciting time to be at Infoblox. Named a Top 25 Cyber Security Company by The Software Report and one of Inc. magazine's Best Workplaces for 2020, Infoblox is the leader in cloud-first networking and security services. Our solutions empower organizations to take full advantage of the cloud to deliver network experiences that are inherently simple, scalable, and reliable for everyone. Infoblox customers are among the largest enterprises in the world and include 70% of the Fortune 500, and our success depends on bright, energetic, talented people who share a passion for building the next generation of networking technologies-and having fun along the way. We are looking for a Data Engineer II to join our Cloud Engineering - AI Integration team in Tacoma, WA or Burnaby, BC, CAN, reporting to the Senior Manager of Software Engineering. In this role, you will drive continuous enhancement and efficiency in our machine learning operations through your expertise in automation, machine learning operations (MLOps) practices, testing strategies, and cloud-based machine learning applications. This is an exceptional opportunity to join a growing, successful, and innovative organization. At Infoblox, you will be able to thrive in a unique work environment that emphasizes career growth, excellence, innovation, and collaboration. You are the ideal candidate if you are passionate about taking on challenges and enjoy working on advancing the reliability and performance of machine learning products. What you'll do: Collaborate with data scientists and software engineers to test and monitor machine learning models in production environments Develop and maintain automated pipelines for model training, testing, QA,and monitoring Implement best practices for version control, model tracking, and reproducibility Design and optimize infrastructure for machine learning workloads on cloud platforms such as AWS, Azure, or GCP Implement continuous integration and continuous deployment (CI/CD) processes for machine learning projects Monitor and maintain deployed models, ensuring high availability, reliability, and performance Troubleshoot and debug issues related to model deployment and performance Stay up to date with advancements in machine learning technologies and tools, and evaluate their applicability to the organization's infrastructure and processes Collaborate with cross-functional teams to define requirements and prioritize tasks What you'll bring: Bachelor's degree or higher in computer science, engineering, or a related field Minimum 5 years of experience, with 3 years of experience in MLOps or related fields Excellent programming skills in languages, such as Python, Spark, or Go Experience with containerization technologies, such as Docker and container orchestration platforms like Kubernetes Proficiency in cloud computing platforms such as AWS, Azure, or GCP Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn Familiarity with generative AI and LLMs, such as GPT, Gemini, or LLama2 Experience with version control systems such as Git Superb problem-solving and troubleshooting skills Excellent communication and collaboration skills, and the ability to work independently and in a team What success looks like: After six months, you will... Successfully deploy multiple machine learning models into production environments using automated pipelines Develop and implement best practices for version control, model tracking, and reproducibility Optimize infrastructure for machine learning workloads, improving scalability and performance Implement CI/CD processes for machine learning projects, reducing deployment time and increasing efficiency Establish monitoring and maintenance procedures, ensuring high availability and reliability of deployed models <