MLOps Engineer

Qualifications

• Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development
• Experience with popular MLOps tools (e.g., Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.)
• Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.)
• Experience in programming languages common to data science such as Python, SQL, etc.
• Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.).
• Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists
• Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC.
• Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc.
• Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value.
• Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking.
• Ability to communicate complex ideas in a concise way. Fluent with popular diagraming and presentation software.
• Demonstrated experience in teaching and/or mentoring professionals.
  

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