Hands-on experience building and deploying LLM-based applications in production.

- Expertise in prompt engineering, few-shot learning, chain-of-thought, and instruction tuning.
- Experience with agentic AI frameworks such as Lang Chain, Lang Graph, Auto Gen, CrewAI, or Semantic Kernel.
- Familiarity with Model Context Protocol (MCP) and context window management strategies.
- Experience implementing guardrails for content filtering, hallucination mitigation, and policy enforcement.
- Knowledge of RAG architectures, vector search, and embedding pipelines.
- Experience designing multi-agent orchestration workflows and task delegation patterns.
- Proficiency with API design and development using REST, GraphQL, or gRPC.
- Familiarity with tool and function calling in LLM APIs such as OpenAI and Anthropic.
- Experience with cloud platforms such as AWS, GCP, or Azure and containerization with Docker and Kubernetes.
- Experience with asynchronous programming, microservices, and event-driven architectures.
- Solid understanding of software design patterns, clean code principles, and test-driven development.

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📌 Artificial Intelligence Engineer (Mississauga)
🏢 TSR Consulting
📍 Mississauga