Senior AI Engineer (GenAI & GCP Specialist)

Position Description Founded in 1976, CGI is among the largest independent IT and business consulting services firms in the world. With 94,000 consultants and professionals across the globe, CGI delivers an end-to-end portfolio of capabilities, from strategic IT and business consulting to systems integration, managed IT and business process services and intellectual property solutions. CGI works with clients through a local relationship model complemented by a global delivery network that helps clients digitally transform their organizations and accelerate results. CGI Fiscal 2024 reported revenue is CA$14.68 billion and CGI shares are listed on the TSX (GIB.A) and the NYSE (GIB). Learn more at cgi.com. Job Title: Senior Data Engineer – Position: Senior AI Engineer (GenAI & GCP Specialist) Experience: 4 - 6 Years Category: Senior Software Development/ Engineering Shift: General Shift Main location: India, Karnataka, Bangalore Position ID: J0426-1668 Employment Type: Full Time

Qualification: Bachelor's degree in Computer Science or related field or higher with minimum 3 years of relevant experience. Your future duties and

We are looking for a highly technical AI Engineer to lead the development and deployment of our Generative AI initiatives. You will be responsible for the end-to-end lifecycle of AI products—from the underlying mathematics of transformer architectures to the cloud-native deployment on Google Cloud Platform (GCP). The ideal candidate bridges the gap between deep academic research (Mathematics and Information Theory) and practical engineering (Fine-tuning, RAG, and MLOps). Core

Model Architecture & Development Generative Modeling: Design and implement solutions utilizing Transformers, Diffusion Models, and VAEs. You will determine when to use decoder-only vs. encoder-decoder architectures based on the use case. LLM Engineering: Optimize Large Language Models through Fine-tuning (LoRA, QLoRA), Prompt Engineering, and Retrieval-Augmented Generation (RAG) to reduce hallucination and improve domain specificity. Algorithmic Optimization: Apply foundations in Linear Algebra and Information Theory to optimize model weights, loss functions, and inference latency. GCP Ecosystem & AI Infrastructure Vertex AI Orchestration: Manage the full ML lifecycle using Vertex AI Pipelines, Training, and Prediction. Utilize Model Garden for model selection and GenAI Studio for rapid prototyping. Cloud Native Deployment: Containerize models for deployment on GKE (Google Kubernetes Engine) or Cloud Run, ensuring high availability and scalable inference. Data & Monitoring: Integrate with BigQuery for data retrieval and use Cloud Logging/Monitoring to track model drift and performance in production.