Job#: 2056866

Job Description:

Hybrid in Detroit OR Remote Fulltime? Description

Join Canopy, a Ford-backed company, at the forefront of engineering advanced threat detection and deterrence products specifically designed for vehicles. Our mission is to eliminate vehicle crime and enhance mobility through cutting-edge consumer hardware, aftermarket connectivity, and AI-driven security solutions. As part of our team, youll be at the forefront of innovation, helping to solve one of today's most pressing challenges with cutting-edge solutions.

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The Staff MLOps Engineer will lead the design, development, and implementation of scalable machine learning operations (MLOps) pipelines and infrastructure. This role is critical for ensuring that machine learning models are efficiently deployed, monitored, and maintained in production environments. The engineer will work closely with machine learning engineers, data scientists/engineers, and platform/SRE engineers to streamline and automate data ingestion, model deployment, ensuring high availability, reliability, and performance of AI-driven solutions. A significant portion of the work will focus on optimizing the end-to-end lifecycle of machine learning models, from development to production.

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Responsibilities:?

• Design, implement, and maintain robust MLOps services and pipelines to automate the deployment and monitoring of machine learning models in production.
• Collaborate with engineers to integrate machine learning models into existing systems and workflows.
• Develop and enforce best practices for CI/CD in machine learning projects, including version control, automated testing, and continuous integration.
• Monitor the performance and health of data and models in production, implementing strategies for retraining, versioning, and rollback when necessary.
• Optimize infrastructure for model training and inference, ensuring scalability, efficiency, and cost-effectiveness.
• Work closely with DevOps teams to ensure seamless integration of MLOps pipelines with cloud or on-premises infrastructure.
• Provide mentorship and guidance to junior engineers and contribute to the development of MLOps strategies and standards across the organization.

Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• 8+ years of experience in software engineering, data engineering, or a related field, with at least 3 years specifically in MLOps.
• Strong programming skills in languages such as Python, Java, or C/C++.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proficiency in DevOps tools and practices, including Docker, Kubernetes, and CI/CD pipelines.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong understanding of version control systems like Git and experience with infrastructure as code (IaC) tools such as Terraform or CloudFormation.
• Excellent problem-solving skills, with the ability to troubleshoot complex production issues.

Preferred Qualifications:

Reside within the Detroit area or nearby, with the ability to work in a hybrid environment and regularly commute to our Detroit office as needed.

Masters degree or Ph.D. in Computer Science, Data Science, Engineering, or a related field.

Experience with advanced MLOps practices such as feature stores, model interpretability, and fairness in AI.

Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka.

Experience with monitoring and observability tools such as Prometheus, Grafana, or ELK stack.

Previous experience leading or mentoring a team of engineers in an MLOps or DevOps context.

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