MLops Engineer

  • Location:

    New York, New York - United States

  • Sector:


  • Job ref:


  • Contact:

    Zack Cheatham

  • Expiry date:


  • Published:

    3 months ago

  • Location: New York, New York
  • Type: Direct Hire
  • Job #1442

MLOps Engineer

Job Description

Location to work

  • Must live in the USA – 100% Remote.

  • Must be able to work within multiple time zones in USA.

We are looking for experienced Machine Learning (ML) Cloud professionals to be part of our Cloud practice. You will be working on industry leading public Cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform to design, develop, and implement AI/ML Solutions, best practices for next generation Marketing solutions leveraging Cloud native and Commercial/Open Source technologies.

Key Responsibilities

  • Exposure in designing and implementing collaborated solutions on GCP/Azure/GCP

  • Develop ML pipelines for automation and orchestrion of the data science life cycle.

  • Work closely with Data Scientists and help optimize and Data Prep, fine tune Model evaluation and model deployment

  • Running machine learning tests and experiments

  • Participate in creating new services capabilities, productized solution offerings and document

  • Ability to transform data science prototypes into robust, scalable products running seamlessly in production



  • 3+ years experience as a Machine Learning Engineer or similar role

  • Deep understanding of Machine Learning concepts (Model Evaluation/ Model Deployment/ Model Monitoring)

  • Deep Knowledge of data processing, manipulation and operationalizing for data consumption

  • 3+ years of experience with Cloud Native Architecture, Docker, Microservices, Kubernetes, EKE/GKE, serverless computing, etc.

  • Strong experience in managing & deploying AI/ML models on Cloud Platform Core Skills : TensorFlow, NumPy, Pandas, Sklearn, Python, PySpark, MLFlow

  • Experience with classical Client and DL models, and designing, building, and maintaining production-grade machine learning workflows, pipelines and models on Kubernetes with Kubeflow/KFserving

  • Hands on experience on Model deployment and Model Monitoring on any public cloud (AWS/Azure/ GCP). GCP experience is an added advantage

  • Outstanding analytical and problem-solving skills 

  • Leading Cloud Certificated, preferred (demonstrating a proficiency to optimally architect a compute, storage, security environment and leverage Cloud automation services). 

  • Ability to work with minimal supervision, making decisions based upon priorities, schedules and an understanding of business initiatives

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