LLM Engineer

Our client is looking for an LLM Engineer to help build and optimize multiple AI agent frameworks that streamline procurement, predict optimal options, and autonomously execute tasks on behalf of users.  This is a 12-month contract with 3 days in office per week in downtown Toronto.

As an integral part of our AI team, you will develop, fine-tune, and deploy large language models, ensuring they deliver accurate and actionable insights. This role requires a combination of strong technical expertise and an ability to collaborate with business teams to drive impactful AI solutions.

Key Responsibilities:

• Develop and implement LLM-powered AI agents to optimize procurement workflows.
• Fine-tune foundation models for domain-specific applications.
• Build and maintain pipelines for data processing, model training, and inference.
• Work closely with business stakeholders to understand requirements and ensure AI solutions align with strategic goals.
• Evaluate model performance and apply prompt engineering, RAG (retrieval-augmented generation), and other techniques to enhance accuracy and efficiency.
• Collaborate with cross-functional teams, including data engineers and procurement specialists, to integrate AI agents into business processes.

Requirements:

• Master's degree in Mathematics, Physics, Computer Science, or a related field (PhD in LLM or NLP research is a plus).
• 3+ years of experience working with LLMs, NLP, or AI-driven automation.
• Proficiency in Python, PyTorch, TensorFlow, LangChain, LangGraph and Hugging Face.
• Experience in fine-tuning large-scale models and optimizing inference efficiency.
• Familiarity with vector databases, knowledge graphs, and prompt tuning.
• Strong problem-solving skills and ability to translate business needs into AI solutions.
• Excellent communication and presentation skills, with the ability to explain AI concepts to non-technical stakeholders.

Nice to Have:

• Experience working with GCP (Google Cloud Platform) and AI/ML tools.
• Knowledge of multi-agent systems and reinforcement learning.

Candidates should be willing to undergo multiple levels of technical assessments.

Thank you for your interest.