Job Description
Lead AI EngineerWhat is the Opportunity?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 ResponsibilitiesAs a Lead AI Engineer, you are an expert qualified 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 ActivitiesEvaluate enterprise software packages and AI platforms, recommending solutions to senior managementLead and mentor colleagues in AI product reviews, pilot projects, and technology adoptionConduct in-depth analysis of business requirements for large-scale, mission-critical projectsPerform feasibility studies and translate complex requirements into robust technical designsDrive adoption of AI-driven practices across engineering teams
What You’ll DoAI Engineering & ImplementationLead the design and development of MCP (Model Context Protocol) servers and tools for seamless AI integrationDesign and implement AI-driven automation solutions,



including LLM-powered automation, prompt engineering, and conversational AIDevelop ML model integration solutions and AI orchestration workflows with monitoring and evaluationIntegrate UiPath with enterprise AI platforms and APIsSupport implementation of agentic automation and advanced AI use cases
AI/ML Expertise & GovernanceIntegrate LLM APIs and apply advanced prompt engineering techniquesDesign and maintain AI workflow orchestration systemsImplement Intelligent Document Processing and NLP solutionsDevelop model evaluation, tuning, and governance frameworksDesign and manage vector databases and RAG (Retrieval-Augmented Generation) systemsSupport responsible AI practices and governance models
AI-Driven Software Development Lifecycle (SDLC)Implement AI spec-driven development practices for your squadContribute to AI/ML solutions that enhance code generation, testing, deployment, and monitoringIntegrate AI technologies into existing SDLC workflows and development toolingCollaborate on governance and responsible AI implementation
Technical Leadership & CollaborationLead technology evaluation and assessment for organizational adoptionCollaborate with enterprise architecture, security, and compliance teams on AI integrationContribute to technical training and knowledge-sharing initiatives across the organization




What You Need to SucceedMust Have10+ years of experience building and operating production software systems at scale2–4 years of hands‑on experience with LLMs and generative AI in production environmentsExpert proficiency in .
NET/C#, Java, Python, SQL, Git, and CI/CD pipelinesDeep knowledge of design patterns (MVC, MVVM), SOA, and architectural concepts;
hands‑on development experience with Azure Integration ServicesExpert-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 managementProven 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 frameworksCloud platform expertise (AWS, Azure, or GCP), including containers (Docker/Kubernetes) and CI/CD pipelinesStrong collaboration and communication skills;
comfortable with code reviews, design discussions, and pair programmingAbility to communicate technical decisions clearly to both technical peers and business stakeholdersUnderstanding of security, data governance, and responsible AI practices
Nice-to-HaveExperience with agentic automation and advanced AI solutions in productionOpen‑source contributions to automation or AI/ML projectsMicrosoft 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, embrace progressive thinking, and work together to deliver trusted solution

📌 Lead Ai Engineer (Winnipeg)
🏢 RBC
📍 Winnipeg