Job Description Lead AI Engineer What is the Prospect? We are seeking an accomplished Lead AI Engineer to design, develop, and implement AI-driven solutions within our software development lifecycle. You will lead enterprise-scale AI integration initiatives, establish best practices, and advance the technical maturity of our engineering organization. This role actively contributes to improving our Agile, Engineering, and DevOps practices.

This position demands exceptional problem-solving skills, deep AI expertise, and proficiency in modern development technologies (.NET/C#, Java, Python). You will effectively communicate and collaborate with engineering teams, business stakeholders, and senior management across multiple projects and organizational boundaries.

Role Level and Responsibilities As a Lead AI Engineer, you are an expert professional with deep technical expertise who contributes to organizational objectives through innovative solutions to complex business challenges. Your primary focus is identifying root causes and implementing solutions to critical business problems while assessing and communicating the technological impact on the business.

Key Activities Evaluate enterprise software packages and AI platforms, recommending solutions to senior management Lead and mentor colleagues in AI product reviews, pilot projects, and technology adoption Conduct in-depth analysis of business requirements for large-scale, mission-critical projects Perform feasibility studies and translate complex requirements into robust technical designs Drive adoption of AI-driven practices across engineering teams

What You’ll Do AI Engineering & Implementation Lead the design and development of MCP (Model Context Protocol)



servers and tools for seamless AI integration Design and implement AI-driven automation solutions, including LLM-powered automation, prompt engineering, and conversational AI Develop ML model integration solutions and AI orchestration workflows with monitoring and evaluation Integrate UiPath with enterprise AI platforms and APIs Support implementation of agentic automation and advanced AI use cases

AI/ML Expertise & Governance Integrate LLM APIs and apply advanced prompt engineering techniques Design and maintain AI workflow orchestration systems Implement Intelligent Document Processing and NLP solutions Develop model evaluation, tuning, and governance frameworks Design and manage vector databases and RAG (Retrieval-Augmented Generation) systems Support responsible AI practices and governance models

AI-Driven Software Development Lifecycle (SDLC) Implement AI spec-driven development practices for your squad Contribute to AI/ML solutions that enhance code generation, testing, deployment, and monitoring Integrate AI technologies into existing SDLC workflows and development tooling Collaborate on governance and responsible AI implementation

Technical Leadership & Collaboration Lead technology evaluation and assessment for organizational adoption Collaborate with enterprise architecture, security, and compliance teams on AI integration Contribute to technical training and knowledge-sharing initiatives across the organization





What You Need to Succeed Must Have 10+ years of experience building and operating production software systems at scale 2–4 years of hands‑on experience with LLMs and generative AI in production environments Expert proficiency in .NET/C#, Java, Python, SQL, Git, and CI/CD pipelines Deep knowledge of design patterns (MVC, MVVM), SOA, and architectural concepts; hands‑on development experience with Azure Integration Services Expert-level expertise in API architecture (REST/SOAP) Advanced working knowledge of agentic AI patterns: tool use, agent orchestration, ReAct, multi‑step reasoning, memory and context management Proven proficiency in LLM API integration, prompt engineering, and RAG systems (vector databases, embeddings, retrieval strategies) Demonstrated experience designing and building MCP servers and tools with agent‑tool integration frameworks Cloud platform expertise (AWS, Azure, or GCP), including containers (Docker/Kubernetes) and CI/CD pipelines Strong collaboration and communication skills; comfortable with code reviews, design discussions, and pair programming Ability to communicate technical decisions clearly to both technical peers and business stakeholders Understanding of security, data governance, and responsible AI practices

Nice-to-Have Experience with agentic automation and advanced AI solutions in production Open‑source contributions to automation or AI/ML projects Microsoft Azure certification (Azure Developer Associate or Azure Solutions Architect Expert) Experience building and training machine learning models (regression, classification, clustering) using frameworks like Scikit-learn, TensorFlow, or PyTorch

What’s In It For You? We thrive on the challenge to be our best,

📌 Lead AI Engineer (Toronto)
🏢 RBC
📍 Toronto