Senior MLOps Engineer

1 month ago


Dallas, United States Artmac Soft LLC Full time
Job DescriptionJob Description

Who we are:

Artmac Soft is a technology consulting and service-oriented IT company dedicated to providing innovative technology solutions and services to the Customers.

Job Description:

Job Title : Senior MLOps Engineer (Machine Learning Operations Engineer)

Job Type : C2C

Experience : 5 to 25 years

Location : Dallas, Texas

We are seeking a skilled and motivated MLOps Engineer to join our dynamic team. The ideal candidate will be responsible for developing, implementing, and maintaining machine learning pipelines and infrastructure, ensuring the efficient and reliable deployment of ML models into production environments. You will collaborate closely with data scientists, software engineers, and IT professionals to optimize and automate the machine learning lifecycle.

Responsibilities:

  • 8+ years experience in DevOps or Operations team
  • Experience with deployment of infrastructure and applications in AWS public cloud (mandatory)
  • Experience with several AWS services, RDS, S3, IAM, Lambda, and Networking.
  • Good understanding of docker and Kubernetes container orchestration (Deploy and administration)
  • Artifactory experience, working repositories. (mandatory)
  • Automation knowledge Ansible, chef, scripting skills.
  • Experience with Jenkins, writing Jenkinsfiles, Jenkins Pipelines, and scripting job DSL in Groovy
  • Scripting knowledge with Python, Perl, or Bash. (mandatory)
  • Airflow pipeline experience is Mandatory.
  • Design, implement, and manage scalable ML model deployment pipelines.
  • Automate the deployment process using CI/CD tools.
  • Monitor, troubleshoot, and maintain deployed models in production.
  • Develop and maintain infrastructure for data ingestion, processing, and storage. Optimize compute resources for cost and performance efficiency.
  • Ensure high availability and scalability of ML infrastructure.
  • Provide support for model retraining and updates.
  • Implement monitoring and logging solutions for model performance and data quality.
  • Optimize the performance of large language models (LLMs) in production environments.
  • Implement techniques for efficient inference and fine-tuning of LLMs.
  • Monitor and improve the scalability and latency of LLM deployments.
Qualification:
  • Bachelor's degree in Computer Science, Engineering, or a related field

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