AI Engineer
Hybrid · Toronto, Ontario, Canada
Job Summary
Design and build agentic frameworks to solve critical sales use cases including client intelligence gathering, pitch content generation, deal summarization, and real-time conversation analysis. Develop conversational AI systems that assist sales professionals in client interactions and help synthesize client intelligence across multiple data sources. Embed Generative AI tools into Sales platforms, enabling seamless workflows for prospect research, proposal generation, and deal tracking. Architect end-to-end AI solutions covering experimentation, model evaluation, and production monitoring to ensure sales tools perform reliably at scale. Guide the team on best practices, conduct code reviews, and mentor team members on Generative AI implementation.
Required Qualifications
- Must-have a PhD or Master's degree in Computer Science, Machine Learning, Deep Learning, or equivalent hands-on experience
- 2-5 years building Deep Learning or Machine Learning models in production environments
- Advanced proficiency in Python and hands-on experience with Generative AI frameworks and architectures
- Deep knowledge of retrieval-augmented generation (RAG), agentic frameworks, context and memory management, and tool/skills integration patterns
- Strong understanding of large language model architectures, inference, fine-tuning, and model deployment
- Experience with Anthropic models and Claude, including code generation capabilities
- In-depth knowledge of embeddings, re-rankers, and vector databases
- Expertise in ML experimentation, model evaluation, and monitoring in production
- Strong foundation in algorithms, data structures, and distributed computing
- Understanding of sales workflows, client intelligence use cases, or familiarity with how sales teams operate
- Embraces an AI-first approach: actively incorporates AI tools and technologies into daily workflows
- Demonstrates curiosity about emerging AI capabilities and applies them thoughtfully to deliver better outcomes for clients and internal stakeholders
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