Lead AI Platform Specialist

Work model: Hybrid, minimum 2 days onsite

Based in Montreal, we have been helping Canadian entrepreneurs manage their small and medium-sized businesses for more than 40 years. We provide all-in-one cloud solutions that simplify payroll and human capital management. Join our dynamic team and help contribute to the success of Canadian businesses.

CGI’s Payroll Services Centre (PSC) is continuing to integrate artificial intelligence across its products and operations. As part of a close-knit organization, our approach is iterative, secure and value-driven, with a focus on delivering robust, fit-for-purpose solutions rather than large-scale transformation programs.

In this context, the PSC is creating a new Lead AI Platform Engineer position. The role will be responsible for structuring and evolving our AI capabilities, both on-premises and in the cloud, while contributing to the delivery of concrete AI use cases in collaboration with IT, Data and Architecture teams.

What we offer
Numerous career development opportunities supported by a professional development plan
Easy access to public transportation and nearby parking options
Rewarding relationships with clients who consistently report a high level of satisfaction
A work environment recognized as one of the best in the country and certified as a Great Place to Work

Role Summary

The Lead AI Platform Engineer is responsible for designing, implementing and evolving a pragmatic, scalable and production-ready AI platform.

The successful candidate will act as the technical owner of the AI platform, with responsibility for architecture decisions, integrations, security, performance, cost management and reliability.

The role combines architecture, integration and production deployment responsibilities, with a strong operational and hands-on technical focus.

We are looking for a versatile professional who can work across the full lifecycle:

Design → Development → Integration → Deployment → Team Support

Roles and Responsibilities
AI Platform and Standards – Implementation and Evolution
Design, deploy and maintain AI platform components, including inference services, project templates, internal libraries and reusable components.
Establish pragmatic standards, including conventions, documentation, best practices, and quality and security requirements.
Ensure operational stability through logging, monitoring, version management, access management and secrets management.
Define operational requirements and transition solutions to IT support teams in accordance with established processes.
End-to-End Delivery of AI Use Cases
Prototype and industrialize high-value AI use cases, such as assistants, document search, information extraction, classification and automation.
Build production-oriented solutions focused on performance, reliability, maintainability and traceability.
Work closely with stakeholders to define requirements, prioritize initiatives and deliver solutions incrementally.
Integration and Operation of Cloud AI Services
Integrate the AI platform and applications with cloud-based AI services such as Azure OpenAI, OpenAI, Azure AI Services or equivalent technologies.
Design secure and compliant integrations, including access control, data protection and internal compliance requirements.
Monitor and optimize usage costs through model selection, caching strategies, quotas and integration best practices.
Autonomy, Technical Leadership and Coaching
Work independently on high-impact initiatives delivered through an iterative approach.
Provide technical leadership and contribute to technology reviews and architectural direction.
Contribute to the AI roadmap, including platform evolution, automation and new AI use cases, in alignment with organizational capabilities and priorities.

Skills and Qualifications
Behavioural Competencies
Results-oriented, with the ability to deliver effectively in a resource-conscious environment.
Strong autonomy, rigor and prioritization skills.
Ability to progressively structure and standardize environments without overengineering.
Strong communication skills and the ability to explain complex concepts to both technical and non-technical stakeholders.
Collaborative mindset and ability to work across multiple teams, including IT, Data, Security and Architecture.
Technical Qualifications
Intermediate to senior profile, ideally with 5+ years of experience in software development or engineering, including significant hands-on AI experience in production environments.
A more junior profile may be considered only if the candidate has already delivered and deployed AI solutions in real-world environments, demonstrates a high level of autonomy, and has a strong ability to learn quickly.
Advanced proficiency in spoken and written French. English, both spoken and written, is considered an asset.
Strong expertise in software development and systems integration, including APIs, services and data processing.
Strong proficiency in Python.
Practical experience with AI solutions, including significant exposure to several of the following:
Large Language Models (LLMs) and API-based integration, such as Azure OpenAI or OpenAI
Retrieval-Augmented Generation (RAG), including embeddings, search, vector databases and pipeline design
AI production practices, including cost control, abuse prevention, security, testing, versioning, monitoring and performance management
Practical DevOps/MLOps knowledge, including CI/CD, containers such as Docker, and basic observability.
Cloud knowledge, ideally Azure, and experience with hybrid architectures are considered assets.

Additional Assets
Demonstrated ability to design robust, scalable and secure AI systems.
Experience with hybrid architectures combining on-premises and cloud environments.
Strong knowledge of APIs, microservices and containerized environments.
Kubernetes and orchestration experience.
Familiarity with search services and vector databases such as pgvector, Azure AI Search, Pinecone or Weaviate.
Knowledge of applied cybersecurity concepts, including IAM, secrets management, network segmentation and compliance.
Experience with on-premises infrastructure, including GPU environments where applicable.