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Lead MLOps Specialist

2 months ago

Dallas, Texas, United States Artmac Soft LLC Full time
Job Overview

About Us:

Artmac Soft LLC is a premier technology consulting firm committed to delivering cutting-edge technology solutions and services tailored to our clients' needs.

Position Summary:

We are looking for an experienced MLOps Engineer to enhance our innovative team. The successful candidate will play a crucial role in the design, implementation, and management of machine learning pipelines and infrastructure, ensuring the seamless and dependable deployment of ML models in production settings. Collaboration with data scientists, software developers, and IT specialists will be essential to streamline and automate the machine learning lifecycle.

Key Responsibilities:

  • Over 8 years of experience in a DevOps or Operations role.
  • Proficient in deploying infrastructure and applications on AWS public cloud (essential).
  • Familiarity with various AWS services, including RDS, S3, IAM, Lambda, and Networking.
  • Strong understanding of Docker and Kubernetes for container orchestration (deployment and management).
  • Experience with Artifactory and repository management (essential).
  • Knowledge of automation tools such as Ansible, Chef, and scripting capabilities.
  • Experience with Jenkins, including writing Jenkinsfiles, Jenkins Pipelines, and Groovy scripting for job DSL.
  • Proficient in scripting languages such as Python, Perl, or Bash (essential).
  • Experience with Airflow for pipeline management is essential.
  • Design, implement, and oversee scalable ML model deployment pipelines.
  • Automate deployment processes using CI/CD methodologies.
  • Monitor, troubleshoot, and sustain deployed models in production environments.
  • Develop and maintain infrastructure for data ingestion, processing, and storage, optimizing computational resources for cost-effectiveness and performance.
  • Ensure the high availability and scalability of ML infrastructure.
  • Provide support for model retraining and updates.
  • Implement monitoring and logging solutions to assess model performance and data quality.
  • Enhance the performance of large language models (LLMs) in production.
  • Apply techniques for efficient inference and fine-tuning of LLMs.
  • Monitor and enhance the scalability and latency of LLM deployments.
Qualifications:
  • Bachelor's degree in Computer Science, Engineering, or a related discipline.