MLOps Engineer

2 days ago

Anywhere in IndiaMultiple Locations, IN Flipped.ai Full time

MLOps Engineer

Job Responsibilities :

Model Deployment :

  • Deploy machine learning models in production environments on cloud platforms such as Azure and AWS.
  • Work closely with data scientists and software engineers to integrate models into existing systems.
Infrastructure Management :
  • Set up and maintain infrastructure for machine learning model deployment.
  • Ensure smooth integration of ML workflows into CI/CD pipelines.
Monitoring and Maintenance :
  • Implement and manage robust monitoring solutions to track model performance, data drift, and other relevant metrics.
  • Proactively identify and address issues related to model degradation or system failures.
Cloud Expertise :
  • Demonstrate expertise in cloud platforms, particularly Azure and/or AWS, for deploying and managing machine learning services.
Automation and Scripting :
  • Develop automation scripts and tools to streamline deployment processes and enhance efficiency.
  • Implement infrastructure as code (IaC) practices for reproducibility and scalability.
Requirements & Qualifications :
  • Experience: 3-8 years of hands-on experience in deploying and monitoring machine learning models.
  • Basic understanding of ML and data science/modeling aspects.
Technical Skills :
  • Proficiency in cloud platforms, especially Azure and/or AWS.
  • Experience with open-source tools for MLOps, such as Kubernetes, Docker, and Apache Airflow and developing and streamlining workflows, building scalable pipelines, etc.
  • Ensuring successful model development, testing, optimization, scaling, monitoring/observability, and governance.
  • Continuous integration and continuous deployment (CI/CD).
  • Knowledge of best practices and processes to promote reproducibility in a centralized manner (cloud/hybrid infrastructure and environment).
  • Strong scripting skills (e.g., Python) for automation and tool development.
Problem-Solving :
  • Strong problem-solving skills with a focus on finding efficient and scalable solutions.
  • Ability to learn new tools on the go and developing best practices is mandatory.

(ref:hirist.tech)

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