AI /ML Engineer

Position Overview We are seeking an experienced AI Lead to architect, develop, and operationalize machine learning and AI solutions across a diverse range of client engagements.This role blends hands-on technical depth with strategic leadership, focusing on the full AI/ML lifecycle — from data engineering and experimentation to scalable deployment on Kubernetes-powered GPU clusters. You'll play a pivotal role in driving open-source AI adoption, mentoring teams, and ensuring CGI's AI delivery aligns with industry best practices in MLOps, data governance, and responsible AI.

Key Responsibilities

. Lead end-to-end AI/ML solution delivery — from business problem definition, data preparation, model design, and training to production deployment and monitoring. . Architect scalable ML pipelines leveraging open-source frameworks such as TensorFlow, PyTorch, scikit-learn, MLflow, and Kubeflow. . 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. . Provide technical mentorship and leadership across project teams and client engagements. . Contribute to AI governance and model explainability frameworks aligned with CGI's responsible AI principles. . Evaluate emerging AI tools, frameworks, and architectures to drive continuous improvement.

Required Qualifications

. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field. . 8+ 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. . Hands-on experience in cloud platforms (AWS, Azure, or GCP) and familiarity with their AI/ML ecosystem services. . Excellent understanding of data pipelines, API integration, and enterprise-scale deployment architectures.

Preferred Skills

. Experience with GPU optimization and frameworks such as CUDA, NVIDIA Triton, or TensorRT. . Exposure to Large Language Models (LLMs) and fine-tuning of open-source models. . Prior experience in consulting environments delivering AI-driven client solutions. . Contributions to open-source ML projects or active participation in AI communities. . Strong communication and stakeholder management skills, with the ability to translate complex AI concepts into business outcomes.

Skills:

Python