Applied AI Engineer — Scalable AI & RAG Pipelines

Worky; Montreal (administrative region); Hybrid; CAD 70,000 - 110,000; Full time

Job summary

Worky is hiring an Applied AI Engineer in Montreal, hybrid (2 days in office). The role focuses on designing, building, and scaling enterprise-grade AI-powered systems.

You will implement features in Python, develop Agentic Workflows, and apply deep learning, NLP, and Generative AI techniques to real-world data. You will drive RAG pipelines, tokenization, and embeddings, while ensuring system observability with MLOps practices.

Qualifications

• Experience as a hands-on software engineer building enterprise-scale, cloud-native systems.
• Advanced proficiency in Python and its ecosystem.
• Experience developing Agentic Workflows using leading frameworks.
• Solid understanding of Deep Learning and Machine Learning fundamentals.
• Strong NLP knowledge and Generative AI fundamentals, especially RAG pipelines.

Responsibilities

• Lead design and development of robust, scalable cloud-native architectures.
• Implement features using Python and modern frameworks.
• Develop Agentic Workflows leveraging industry frameworks.
• Apply DL/ML concepts to solve real-world problems.
• Design scalable UIs with modern JavaScript frameworks.
• Leverage NLP for Insights from diverse data sources.
• Drive GenAI solutions with Transformer models, embeddings, and tokenization.
• Optimize RAG pipelines and ensure observability with MLOps tools.
• Evaluate AI models using appropriate metrics.
• Navigate multiple priorities in a fast-moving environment.
• Contribute to a collaborative, innovative team culture.

Skills

Python

Agentic Workflows

Deep Learning

NLP

Generative AI (RAG)

MLOps/Observability

Evaluation Metrics

Project Management

Job description

Worky is hiring an Applied AI Engineer in Montreal, hybrid (2 days in office). The role focuses on designing, building, and scaling enterprise-grade AI-powered systems.

You will implement features in Python, develop Agentic Workflows, and apply deep learning, NLP, and Generative AI techniques to real-world data. You will drive RAG pipelines, tokenization, and embeddings, while ensuring system observability with MLOps practices.