Machine Learning DevOps Engineer
As a Machine Learning DevOps Engineer, you will work on deploying, scaling, and optimizing backend algorithms, robust and scalable data ingestion pipelines, machine learning services, and data platforms to support analysis on vast amounts of text and analytics data. You will apply your technical knowledge and Big Data analytics on billions of online content data points to solve challenging marketing problems. ML DevOps Engineers are integral to the success of VideoLogic.
- Design and build scalable machine learning services and data platforms.
- Utilize benchmarks, metrics, and monitoring to measure and improve services.
- The system currently processes data on the order of tens millions of jobs per day.
- Research, design, implement and validate cutting-edge algorithms to analyze diverse sources of data to achieve targeted outcomes.
- Work with data scientists to implement ML, AI and NLP techniques for article analysis and attribution.
- Work with technologies like Python, R, Ruby, Scala, Redis, ElasticSearch, Apache Spark, Kubernetes, Docker, etc.
- BS, MS, or Ph.D in Computer Science or related field and/or equivalent experience in the space.
- Software engineering experience.
- Familiarity with frameworks and models such as TensorFlow, TF Serving, ONNX Runtime, BERT, Kubeflow, MLflow
- Experience with machine learning pipelines and deployment.
- DevOps: 3 years (Required)
- Machine learning: 3 years (Preferred)
- AWS: 1 year (Required)
- Docker: 1 year (Required)
- Kubernetes: 1 year (Preferred)
- Montreal or Remote