Senior AI Engineer to design, build, and productionize agentic AI and LLM-powered solutions, including RAG, multi-step reasoning, and MCP server integration, for a digital banking client.

Deliver and Scale Agentic AI Solutions: Design, build, and productionize LLM-powered and agentic applications, including retrievalaugmented Work hands-on to de-risk complex problems by writing, reviewing, and operating production-grade AI systems. Partner closely with product, data, and engineering stakeholders to deliver AI capabilities that drive Apply strong fundamentals in structured and unstructured data, distributed systems, and service Design and operate secure, low-latency services and microservices with modern authentication and Contribute to architectural discussions, platform capabilities, and evolving best practices for AI Collaborate with platform and security partners to ensure systems meet enterprise risk, compliance, Influence Technical Direction and Engineering Culture Take ambiguous problems and translate them into clear technical solutions, communicating trade-offs Model a culture of engineering excellence, inclusion, and continuous learning — digging into root Mentor peers through code reviews and design discussions, raising the bar for quality, ownership, and long-term thinking. Extensive experience in Python and its core data science libraries (e.g., Hands-on experience building LLM-powered applications — retrieval, agents, structured outputs, Hands-on experience building and consuming MCP servers (designing endpoints, schemas, access Deep understanding of structured and unstructured data management and their corresponding Proven experience in automated testing,



including unit and functional testing, and the ability to develop test strategies and design automation frameworks.

Experience with Agentic AI frameworks and designing multi-step AI reasoning processes Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., Experienced with building and deploying NLP and voice response applications (including IVR and Familiarity with Google's Vertex AI tech stack Experience building applications with modern web component frameworks (such as React & Angular Deliver and Scale Agentic AI Solutions: Design, build, and productionize LLM-powered and agentic applications, including retrievalaugmented Work hands-on to de-risk complex problems by writing, reviewing, and operating production-grade AI systems. Partner closely with product, data, and engineering stakeholders to deliver AI capabilities that drive Apply strong fundamentals in structured and unstructured data, distributed systems, and service Design and operate secure, low-latency services and microservices with modern authentication and Contribute to architectural discussions, platform capabilities,



and evolving best practices for AI Collaborate with platform and security partners to ensure systems meet enterprise risk, compliance, Influence Technical Direction and Engineering Culture Take ambiguous problems and translate them into clear technical solutions, communicating trade-offs Model a culture of engineering excellence, inclusion, and continuous learning — digging into root Mentor peers through code reviews and design discussions, raising the bar for quality, ownership, and long-term thinking. Extensive experience in Python and its core data science libraries (e.g., Hands-on experience building LLM-powered applications — retrieval, agents, structured outputs, Hands-on experience building and consuming MCP servers (designing endpoints, schemas, access Deep understanding of structured and unstructured data management and their corresponding Proven experience in automated testing, including unit and functional testing, and the ability to develop test strategies and design automation frameworks.

Experience with Agentic AI frameworks and designing multi-step AI reasoning processes Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., fraud detection, credit risk scoring, or customer segmentation Experienced with building and deploying NLP and voice response applications (including IVR and contact center intelligence) Familiarity with Google's Vertex AI tech stack Experience building applications with up-to-date web component frameworks (such as React & Angular AI may be used in evaluating candidates.

📌 Senior AI Engineer to design, build, and productionize agentic AI and LLM-powered solutions, includi (Toronto)
🏢 S.i. Systems
📍 Toronto