Responsibilities
- Lead the machine learning discipline by setting engineering standards, promoting consistency, and aligning AI initiatives with organizational objectives.
- Design and manage end-to-end AI/ML workflows, including data collection, feature development, model training, deployment, and ongoing performance tracking.
- Guide the creation and adaptation of large language model solutions using open-source frameworks like Mistral and LLaMA, deployed across hybrid cloud and on-premise systems while meeting privacy, speed, and efficiency requirements.
- Develop and oversee natural language processing systems for tasks such as text classification, semantic search, document analysis, and information retrieval.
- Build robust, production-ready AI services with highly available APIs using modern frameworks such as FastAPI.
- Manage the deployment of AI models to cloud environments like Azure or AWS, as well as to edge devices including NVIDIA Jetson Orin and Xavier NX for real-time inference.
- Take ownership of automating infrastructure setup to ensure consistent, scalable, and reproducible machine learning environments using tools such as Terraform, Ansible, and Docker.
- Design and integrate AI components into event-driven and serverless architectures like Azure Functions and Event Grid, ensuring system resilience, scalability, and monitoring capabilities.
- Provide technical guidance and mentorship to early and mid-career engineers, promoting best practices in MLOps, model reproducibility, and responsible AI.
Work Arrangement
On-site — ARHS – Part of Accenture office
Other
- This position includes in-person requirements to support collaboration, professional growth, and strong relationships with clients, coworkers, and communities.
- The employer is committed to accommodating individual work-life balance needs as much as possible.
- Fluency in English or French is required, with proficiency in both languages considered an advantage.
