1 year ago
Toronto, CanadaMid Level
Responsibilities
- Deliver end-to-end LLM-powered software by understanding customer pain points, scoping product specifications, and designing and building solutions.
- Benchmark models and develop customer evaluations to identify model weaknesses.
- Develop and deploy search systems such as RAG and DeepSearch to improve model performance, grounding, and use of enterprise knowledge.
- Implement and optimize fine-tuning and alignment techniques for large models using domain-specific data.
- Ensure the quality, reliability, security, and scalability of models and agentic systems through careful implementation and continuous monitoring.
- Integrate AI components into a scalable platform and build multi-model workflows for task automation.
Requirements
- Bachelor’s or master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
- Strong contribution record on GitHub and a GitHub link included with the application.
- Experience with large language or multimodal models and their applications, as well as search systems.
- Strong attention to detail and demonstrated prioritization of quality, reliability, and security in technical work.
- Proficiency in Python, Rust, TypeScript, or Go and relevant machine learning frameworks such as PyTorch or JAX.
- Ability to design, chain, or orchestrate multiple models, especially LLMs, into multi-step pipelines or task-automation workflows.
- Preferred qualifications include experience with agentic AI products, AWS, GCP, Azure, MLOps, distributed training and inference, system design, API development, scalable AI infrastructure, enterprise integration patterns, data security, HTTP, and WebRTC.
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