We are looking for LLM Solutions Developer for a contract to Hire role in Mississauga.

Its mandatory if you clear Karat test.

We are looking for a talented and forward-thinking LLM Solutions Developer to join our AI Engineering team. In this role, you will design, build, and deploy production-grade solutions powered by Large Language Models (LLMs). You will work at the intersection of cutting-edge AI research and real-world software engineering, delivering intelligent, reliable, and scalable agentic systems.

Key Responsibilities

- Design & Develop LLM-Based Solutions: Architect and implement end-to-end applications leveraging LLMs (e.G., GPT-4, Claude, Gemini, Llama) for tasks such as reasoning, summarization, code generation, and decision support.
- Agentic AI Systems: Build autonomous and semi-autonomous AI agents capable of multi-step reasoning, tool use, and goal-directed behavior using frameworks such as LangGraph, AutoGen, CrewAI, or custom implementations.
- Orchestration: Design and manage complex LLM orchestration pipelines, including multi-agent workflows, task routing, memory management, and context handling.
- Model Context Protocol (MCP): Implement and integrate MCP-compliant architectures to enable structured, context-aware communication between models, tools, and external systems.
- Guardrails & Safety: Integrate guardrail frameworks (e.G., NeMo Guardrails, Guardrails AI, custom rule engines) to enforce output safety, factual accuracy, policy compliance, and ethical AI standards.
- API Development & Integration: Design and expose RESTful or gRPC APIs for LLM-powered services;
integrate with third-party APIs, enterprise systems, and data sources.
- Tool & Plugin Development: Build custom tools, plugins, and function-calling integrations that extend LLM capabilities (e.G., web search, database queries, code execution, document retrieval).
- RAG Pipelines:



Develop Retrieval-Augmented Generation (RAG) systems using vector databases (e.G., Pinecone, Weaviate, pgvector) and embedding models.
- Evaluation & Observability: Implement LLM evaluation frameworks, tracing (e.G., LangSmith, OpenTelemetry), and monitoring dashboards to ensure quality, performance, and reliability.
- Collaboration: Work closely with product managers, data scientists, and platform engineers to translate business requirements into robust AI solutions.
- Documentation: Produce transparent technical documentation, architecture diagrams, and runbooks for all developed systems.

Required Skills & Experience

Core LLM & AI

- Hands-on experience building and deploying LLM-based applications in production environments
- Deep understanding of prompt engineering, few-shot learning, chain-of-thought, and instruction tuning
- Experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar)
- Familiarity with Model Context Protocol (MCP) and context window management strategies
- Experience implementing guardrails for LLM outputs (content filtering, hallucination mitigation, policy enforcement)
- Knowledge of RAG architectures, vector search, and embedding pipelines

Orchestration & Infrastructure

- Experience designing multi-agent orchestration workflows and task delegation patterns
- Proficiency with API design and development (REST, GraphQL, or gRPC)
- Familiarity with tool/function calling patterns in LLM APIs (OpenAI function calling, Anthropic tool use, etc.)
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)

Software Engineering

- Experience with asynchronous programming, microservices, and event-driven architectures
- Solid understanding of software design patterns, clean code principles, and test-driven development
- Version control with Git and CI/CD pipeline experience
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📌 Llm Solutions Developer (Mississauga)
🏢 Open Systems Technologies
📍 Mississauga