IT Support Analyst III (AI/ML)

TD•Toronto, ON

3d•CA$59,500 - CA$84,000•Onsite

About The Position

The Junior AI Engineer supports the development, deployment, and ongoing support of AI/ML applications and AI-powered agents. This role contributes to building intelligent systems using modern frameworks, assists in operationalizing AI capabilities, and ensures reliability and performance of AI solutions in production. The individual will work under guidance from senior engineers while gaining hands-on experience in agent-based architectures, retrieval-augmented generation (RAG), and enterprise AI platforms.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
Working knowledge of Python for AI/ML development
Foundational understanding of machine learning concepts and NLP
Familiarity with AI frameworks such as LangChain, LangGraph, or similar
Basic understanding of RAG architectures and LLM-based applications
Knowledge of REST APIs and software engineering fundamentals
Experience with data manipulation (e.g., SQL, Pandas)

Nice To Haves

Exposure to LLM application development and agent-based systems
Exposure to predictive AI models a big plus
Experience with vector databases
Familiarity with cloud platforms (Azure, AWS, GCP)
Understanding of MLOps / LLMOps practices (deployment, monitoring, evaluation)
Exposure to prompt engineering, embeddings, context engineering and model evaluation techniques
Awareness of Responsible AI, governance, and data privacy considerations

Responsibilities

Assist in building, deploying, and maintaining AI/ML applications and services
Provide L2/L3-style support for AI systems, including incident triage, debugging, and performance tuning
Monitor model and application performance (accuracy, latency, cost) and support optimization efforts
Maintain documentation and runbooks for AI solutions to support operational readiness
Develop and enhance AI agents using frameworks such as LangChain and LangGraph
Contribute to the design of agent workflows, tool integrations, and orchestration logic
Support prompt engineering, context management, and structured output design
Assist in integrating agents with APIs, enterprise systems, and data sources
Support the development of RAG pipelines, including data ingestion, embedding, indexing, and retrieval
Work with vector databases and search mechanisms to improve response relevance
Assist in evaluating and improving grounding, hallucination control, and response quality
Collaborate with data scientists, engineers, and product teams to deliver AI use cases
Participate in Agile ceremonies and contribute to iterative delivery
Stay current with emerging AI tools, frameworks, and best practices