MLOps Engineer

LVT (LiveView Technologies) is on a mission to enhance safety and security through innovative technology solutions. As a MLOps Engineer III, you will be responsible for developing and maintaining infrastructure for machine learning operations, providing leadership and mentorship to a team, and ensuring the integration of ML workflows across various platforms.

Responsibilities:

• Provide technical leadership and mentorship to a growing team of engineers working on machine learning infrastructure
• Help set the technical direction, define best practices, and drive the adoption of modern ML Ops methodologies and technologies
• Design, build, and maintain scalable and robust ML Ops infrastructure for cloud and edge deployments
• Evaluate and make build vs. buy decisions for ML tools and platforms
• Develop and optimize data pipelines to support machine learning workflows
• Integrate and manage machine learning tools for model training, validation, and deployment
• Oversee the deployment of machine learning models to cloud environments and edge devices
• Ensure robust and reliable deployment processes, including continuous integration and continuous deployment (CI/CD)
• Implement observability solutions to monitor the performance and health of ML models and infrastructure
• Proactively identify and address issues in ML pipelines and deployments
• Collaborate with cross-functional teams such as data scientists, software engineers, and product managers to ensure seamless integration and successful delivery of ML products
• Foster effective communication channels and promote a culture of collaboration and knowledge sharing
• Drive continuous improvement initiatives to enhance ML Ops processes, productivity, and efficiency
• Identify bottlenecks, streamline workflows, and implement tools and methodologies to optimize the ML development lifecycle

Requirements:

• Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical/Computer Engineering, or a related field
• 5-7+ years of professional experience in software engineering, with a focus on ML Ops or related fields
• Strong expertise in cloud platforms (e.g., AWS, GCP, Azure) and edge computing
• Proficiency in programming languages such as Python, GoLang, or C++
• Experience with ML Ops tools and frameworks (e.g., Kubeflow, MLflow, PyTorch, etc)
• Excellent problem-solving, debugging, and analytical skills, with the ability to navigate complex technical challenges
• Strong interpersonal and communication skills, with the ability to collaborate effectively with diverse stakeholders
• Demonstrated ability to thrive in a fast-paced and dynamic environment, managing multiple priorities simultaneously
• Track record of delivering high-quality machine learning infrastructure and/or products on time and within budget
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes)
• Familiarity with data engineering and big data technologies
• Knowledge of security best practices in ML Ops and cloud deployments
• Experience with monitoring and observability tools (e.g., Prometheus, Grafana)