Senior Agentic AI Engineer

Senior Agentic AI Engineer

Location: Toronto, ON

Work Arrangement: Hybrid – 50% Remote

Employment Type: Long term contract

Interview: 3 rounds

Job Description

We are seeking a Senior Agentic AI Engineer to design, develop, and deploy production-grade AI applications using Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), and Google Cloud Platform (GCP).

The ideal candidate will have strong Python development skills, hands-on experience building agentic AI solutions, and the ability to troubleshoot complex RAG pipelines, evaluate LLM application quality, and design scalable cloud-based architectures.

This role requires solid software engineering fundamentals and the ability to take AI solutions from design and development through deployment, monitoring, and continuous improvement.

Key Responsibilities

• Design, develop, and maintain agentic AI applications and LLM-powered solutions for enterprise use cases.
• Build and orchestrate multi-step AI workflows using LangGraph, LangChain, or similar frameworks.
• Develop RAG pipelines using embeddings, vector search, and retrieval optimization techniques to deliver accurate, relevant, and grounded responses.
• Troubleshoot RAG performance issues across document ingestion, chunking, embeddings, retrieval, reranking, context construction, and response generation.
• Design and implement evaluation strategies for LLM and agentic AI applications, including retrieval quality, answer relevance, groundedness, hallucination detection, and regression testing.
• Implement LLM observability, tracing, monitoring, and troubleshooting using Langfuse or comparable platforms.
• Develop and integrate MCP-based tools and services to connect AI agents with enterprise systems, APIs, and data sources.
• Build RESTful APIs and backend services using Python frameworks such as FastAPI or Flask.
• Leverage Google Cloud services, particularly Vertex AI and BigQuery, to build and integrate cloud-based AI solutions.
• Implement prompt engineering, tool calling, structured outputs, context management, and appropriate human-in-the-loop controls.
• Apply software engineering best practices, including automated testing, version control, CI/CD, and production deployment.
• Implement security, data privacy, responsible AI, and governance controls appropriate for enterprise AI applications.
• Collaborate with product managers, architects, data engineers, and other stakeholders in an Agile delivery environment.
• Troubleshoot production issues and continuously improve application reliability, performance, scalability, and response quality.

Required Skills and Experience

• 5+ years of professional software development experience.
• 2+ years of hands-on experience developing agentic AI or LLM-powered applications.
• Strong Python programming and software engineering fundamentals.
• Demonstrated experience building, deploying, and supporting LLM-powered applications.
• Hands-on experience with LangGraph, LangChain, or a similar AI application framework.
• Experience with Langfuse or another LLM observability platform.
• Understanding of Model Context Protocol (MCP) and its use in connecting AI agents with tools and enterprise systems.
• Hands-on experience with Google Cloud services, particularly Vertex AI and BigQuery, with the ability to explain architecture decisions and service integrations.
• Experience developing RAG solutions using embeddings and vector search.
• Strong understanding of RAG troubleshooting, retrieval optimization, and techniques for improving answer quality and grounding.
• Experience evaluating LLM and agentic AI applications using measurable metrics, test datasets, and regression testing.
• Experience developing RESTful APIs using FastAPI, Flask, or a similar framework.
• Understanding of prompt engineering, tool calling, structured outputs, context management, and multi-step agent workflows.
• Experience with automated testing, Git, CI/CD, and production deployment practices.
• Understanding of data privacy, AI safety, responsible AI principles, and enterprise application security.
• Experience working with Agile methodologies.
• Excellent verbal and written communication skills, including the ability to explain technical designs, troubleshooting approaches, and engineering trade-offs.