About Verantos

Verantos (https://verantos.com) is a market leader in high accuracy real-world evidence (RWE) generation. The Verantos RWE platform integrates heterogeneous real-world data sources and generates evidence with the accuracy necessary for regulatory and reimbursement use. The Verantos RWE platform leverages data science and artificial intelligence along with advanced data sources such as electronic health records (EHR) to generate RWE capable of supporting complex clinical studies. Some of the largest biopharma companies in the world are Verantos customers.

We use a heterogeneous tech stack on AWS with data processing, artificial intelligence, workflow, and analytic components.

Job description

As an MLOps engineer, you will be an integral part of our AI/ML team. You will help this team to streamline the process of getting models from proposed ideas to running in production environments.


  • As an MLOps Engineer, you will be responsible for designing, implementing, and maintaining machine learning infrastructure, pipelines, and workflows.
  • Leverage your Python skills to develop and optimize code for machine learning workflows, data processing, and automation scripts.
  • Provide support for machine learning tasks, including data pre-processing, feature engineering, model training, and evaluation.
  • Utilize your expertise in Azure, GCP, or AWS to manage and optimize cloud infrastructure, ensuring efficient and scalable machine learning workflows.
  • Work closely with cross-functional teams including data scientists, software engineers, and DevOps specialists to align MLOps initiatives with business objectives and technical requirements.
  • Stay updated on the latest trends and advancements in MLOps, cloud computing, machine learning, and generative AI, and apply new knowledge to enhance our AI capabilities.


  • Bachelor’s degree in CS or ECE.
  • Strong proficiency in Python.
  • 4+ years experience in cloud engineering, with expertise in any of the cloud providers viz. AWS, Azure, or GCP
  • At least 1 year experience in MLOps, machine learning engineering, or a related role. Production level implementation and deployment for AI/ML use case.
  • Experience with Docker, Kubernetes (EKS/AKS/GKE)
  • Experience in MLOps tools such as MLFlow, Sagemaker, Kubeflow, etc.
  • Strong communication skills and ability to communicate analytical and technical content in an easily understandable way

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