Description

You will design and implement the engineering infrastructure and MLOps pipelines required to support enterprise machine learning systems at scale.

Responsibilities

• Build and manage MLOps pipelines on AWS and Azure to operationalize machine learning models.
• Design data pipelines and engineering infrastructure to transform offline models into production-ready systems.
• Orchestrate CI/CD pipelines using GitLab CI and GitHub Actions to apply software engineering rigor to ML projects.
• Manage cloud-based infrastructure using Docker and Kubernetes for scalable application deployment.
• Develop custom integrations between cloud-based systems via APIs and evaluate new technologies to improve system reliability.

Required Skills

• 5+ years of experience in software engineering or related fields.
• Proficiency in Python and Linux administration.
• Hands-on experience with Cloud Computing (AWS, Azure) and database systems.
• Experience with containerization using Docker and Kubernetes.
• Working knowledge of CI/CD tools including GitLab and GitHub.
• Familiarity with data-oriented workflow orchestration frameworks such as Kubeflow, Airflow, or Argo.
• Exposure to machine learning methodologies and deep learning frameworks like PyTorch, TensorFlow, or Keras.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Ability to design scalable tools and services for machine learning training and inference.

Preferred Skills

• Experience with NLP and LLM infrastructure.
• Knowledge of Network Operations and load-balancing solutions.

Key Skills

Education

Any Graduate