Senior Machine Learning Operations Engineer

1 week ago

Santa Clara, United States LigaDATA Full time

Role Overview:

As a Machine Learning Operations (MLOps) Engineer at Ligadata, you will play a key role in developing, training, monitoring & refining ML models as well as developing and maintaining the infrastructure and processes necessary for seamless deployment and scaling of machine learning models. You will collaborate with cross-functional teams to ensure the reliability, efficiency, and performance of our machine learning models & systems.

Responsibilities:

1) MLOps Infrastructure:

  • Design, implement, and manage scalable MLOps infrastructure for machine learning model deployment.
2) Machine Learning Operations Framework:
  • Design, implement, and operate an MLOps framework.
3) Tools Development:
  • Design, develop, integrate, and deploy tools for Model Monitoring, Model Governance, Model Deployment, Data Management, and Feature Engineering.
4) Data Science & ML Model Management:
  • Review, refactor, optimize, containerize, deploy, version, and monitor data science & ML models.
  • Test, validate, and automate model testing processes.
5) Communication:
  • Communicate and document processes with software developers, data scientists, data engineers, and architects.
6) Cutting-edge Technology:
  • Work with cutting-edge LLMs to advance generative AI and redefine professional services.
7) Model Deployment and Monitoring:
  • Deploy machine learning models and manage version control.
  • Implement monitoring tools for tracking and optimizing model performance.
8) Automation and CI/CD:
  • Develop and maintain CI/CD pipelines for machine learning models.
  • Implement version control best practices for collaboration among data science and engineering teams.
9) Collaboration:
  • Collaborate with data scientists, data engineers, and software developers to integrate ML models into applications.
  • Ensure seamless communication and alignment between data science and operational teams.
10) Scalability and Optimization:
  • Optimize machine learning workflows for scalability and efficiency.
  • Identify and address bottlenecks in deployment and operation of ML systems.
Qualifications:

1) Education:
  • Bachelor's/Master's in Computer Science, Data Science, or related field.
2) Experience:
  • Proven ML model building, training, and MLOps experience.
  • Proficiency in diverse ML models, LLMs, GenAI, and Python automation.
  • Familiarity with Java, Scala, and containerization tools (Docker, Kubernetes).
  • Cloud platform experience (Azure, GCP) for ML development.
  • Strong grasp of machine learning concepts and frameworks.
3) Preferred Skills:
  • Knowledge of MLFlow, TensorFlow, PyTorch.
  • Experience with monitoring tools (Prometheus, ELK stack).
  • Understanding of security best practices in ML environments.

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