AI Engineer

 Design and deploy AI workloads on containerized environments using Docker and Kubernetes, optimizing GPU utilization for training and inference.
. Collaborate with data engineers, cloud architects, and business consultants to integrate AI capabilities into enterprise systems.
. Establish and maintain MLOps practices including version control, CI/CD, experiment tracking, and automated retraining.
. Contribute to AI governance and model explainability frameworks aligned with CGI's responsible AI principles.
. Evaluate emerging AI tools and frameworks to drive continuous improvement.


Required Qualifications

. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field.
. 4+ years of experience in AI/ML engineering, data science, or applied machine learning roles.
. Strong proficiency in Python and open-source ML libraries (TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.).
. Proven experience across the complete ML lifecycle — from data preprocessing and model training to serving and monitoring.
. Experience with MLOps frameworks such as MLflow, DVC, Airflow, or Kubeflow.
. Working knowledge of containerization and orchestration (Docker, Kubernetes), including running GPU-based ML workloads.
. Working knowledge of AI as a service (AWS, Azure, or GCP) and familiarity with their AI/ML ecosystem services.
. Excellent understanding of data pipelines, API integration, and enterprise-scale deployment architectures.