Mid-Level Full-Stack AI Engineer

CGI is seeking a Mid-Level Full-Stack AI Engineer to design, develop, and deploy scalable AI-powered applications. The role combines backend services, APIs, data pipelines, machine learning and generative AI integrations, and frontend experiences, while leading features, contributing to architecture, and mentoring junior engineers.

Responsibilities:

• Lead the design, development, and deployment of end-to-end AI-powered applications, including backend services, APIs, data pipelines, model integrations, and user-facing interfaces
• Architect, build, and maintain scalable, secure, and high-performance backend services using Python and develop responsive frontend applications using Angular
• Design and implement generative AI, large language model (LLM), natural language processing (NLP), document classification, optical character recognition (OCR), and conversational AI solutions
• Lead machine learning model inference, training, fine-tuning, and optimization efforts to deliver reliable, production-ready AI capabilities
• Develop and optimize RESTful APIs, system integrations, and database solutions to support seamless interoperability, performance, and scalability across AI platform components
• Implement and maintain CI/CD pipelines and containerized deployments using Docker, Kubernetes, and cloud technologies to support efficient and reliable software delivery
• Collaborate with product managers, data scientists, UI/UX designers, and engineering teams to translate business requirements into technical designs and high-impact AI-driven features
• Lead the technical development of client-focused AI use cases leveraging CGI's AI Platform, driving assigned features from solution design through production deployment
• Monitor, troubleshoot, and optimize production applications to ensure system availability, security, reliability, performance, and operational efficiency
• Mentor junior engineers, participate in architecture and design reviews, provide technical guidance, and champion software engineering best practices across the development lifecycle

Requirements:

• Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical discipline, or equivalent practical experience
• 2+ years of professional experience in full-stack application development, with strong proficiency in Python for backend development and Angular for frontend development
• 2+ years of hands-on experience designing and developing AI-powered products or solutions involving generative AI, NLP, document classification, chatbots, OCR, or related intelligent automation technologies
• Demonstrated experience with machine learning concepts, LLM inference, model integration, and the development and deployment of AI-enabled applications
• Strong experience designing and developing RESTful APIs, integrating distributed application components, and applying software architecture and system design principles
• Hands-on experience with Docker, Kubernetes, and container orchestration concepts, including deployment and management of applications in production environments
• Proficiency with relational and non-relational databases, including database design, querying, performance tuning, and optimization techniques
• Experience leveraging cloud platforms such as Microsoft Azure or Google Cloud Platform (GCP) to develop, deploy, manage, and scale AI applications
• Knowledge of secure software development practices, data privacy regulations, compliance requirements, and responsible AI principles
• Strong analytical, troubleshooting, communication, and collaboration skills, with demonstrated ownership of technical deliverables, the ability to guide junior team members, and adaptability in a fast-paced, evolving technology environment
• Experience developing production-grade applications using LLM frameworks, orchestration libraries, or AI development tools such as LangChain, Hugging Face, or similar technologies
• Hands-on experience implementing retrieval-augmented generation (RAG), vector databases, embeddings, prompt engineering, or AI agent architectures
• Experience with model fine-tuning, model evaluation, MLOps practices, and monitoring AI models in production environments
• Experience building and maintaining automated CI/CD pipelines, testing frameworks, and deployment workflows using GitHub Actions, Azure DevOps, or similar tools
• Experience designing and deploying AI-powered solutions using Microsoft Azure AI services, Google Cloud AI services, or related cloud-native AI technologies
• Demonstrated experience leading technical feature development, mentoring engineers, contributing to architectural decisions, and delivering customer-focused AI solutions