Machine Learning Engineer

Role : LLM Engineer

Location : Toronto, ON

Long-term Contract

Job Description:

The Opportunity

Join our Canadian Artificial Intelligence Team (AIT) as an LLM Engineer, where you'll lead the development of cutting-edge Generative AI solutions using OpenAI models and the LangChain framework. This role is ideal for someone passionate about building real-world applications with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic systems. You’ll work at the intersection of AI research and engineering, transforming innovative ideas into scalable, production-ready solutions.

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Key Responsibilities

• Design, build, and deploy LLM-powered applications using OpenAI APIs, LangChain, and related frameworks.

• Develop RAG pipelines, agentic workflows, and chatbot systems that integrate with enterprise data sources.

• Fine-tune and optimize LLMs for specific business use cases, ensuring high performance and minimal hallucinations.

• Implement prompt engineering strategies and evaluation frameworks to improve model outputs.

• Integrate LLMs with cloud infrastructure (Azure, Databricks) and tools like Azure Cognitive Search, VectorDBs (e.g., FAISS, Pinecone), and Semantic Kernel.

• Collaborate with cross-functional teams to scope, prototype, and productionize AI solutions.

• Apply LLMOps and MLOps best practices for model deployment, monitoring, and lifecycle management.

• Stay current with advancements in LLMs, GenAI frameworks, and open-source ecosystems.

Candidate Requirements/Must Have Skills:

1. 2+ years of experience in NLP, LLMs and Generative AI.

2. Working knowledge of Model Context Protocol (MCP) and its application in LLM-based systems.

3. Hands-on experience with OpenAI APIs, LangChain, and RAG architectures.

4. Proficiency in Python and related libraries

5. Experience with cloud platform (Azure, AWS, or GCP), especially, Databricks, and Cognitive Search.

6. Familiarity with Vector Databases (e.g., FAISS, Pinecone, Weaviate) and agent frameworks (e.g., Autogen).

7. Solid understanding of prompt engineering, LLM fine-tuning, and evaluation techniques.

8. Experience with DevOps/MLOps tools like Git, Docker, MLflow, and Kubernetes.

9. Strong communication skills and the ability to translate technical concepts into business value.

Nice-To-Have Skills:

1. Experience with Streamlit, Flask, or JavaScript for building interactive frontends.

Education:

• Bachelor's degree in a technical field such as computer science, computer engineering or related field.