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.

Is this role right for you? In this role, you will: Deliver and Scale Agentic AI Solutions: Design, build, and productionize LLM-powered and agentic applications, including retrievalaugmented generation (RAG), multi-step reasoning workflows, structured outputs, and prompt safety. Build and consume MCP servers, defining schemas, endpoints, and access boundaries that enable safe, scalable tool use.

Work hands-on to de-risk complex problems by writing, reviewing, and operating production-grade AI systems.

Architect

Secure, Reliable, and Observable Systems: Partner closely with product, data, and engineering stakeholders to deliver AI capabilities that drive tangible outcomes. Apply strong fundamentals in structured and unstructured data, distributed systems, and service integration. Ensure systems are testable, observable, and resilient, with automated testing and clear operational feedback loops.

Design and operate secure, low-latency services and microservices with modern authentication and authorization. Contribute to architectural discussions, platform capabilities, and evolving best practices for AI development. Collaborate with platform and security partners to ensure systems meet enterprise risk, compliance, and operational standards Influence Technical Direction and Engineering Culture Take ambiguous problems and translate them into clear technical solutions, communicating trade-offs and constraints.

Model a culture of engineering excellence, inclusion, and continuous learning — digging into root causes and sharing durable lessons. Mentor peers through code reviews and design discussions, raising the bar for quality, ownership, and long-term thinking.

Required Qualifications: Extensive experience in Python and its core data science libraries (e.g., Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn). Hands-on experience building LLM-powered applications — retrieval, agents, structured outputs, Hands-on experience building and consuming MCP servers (designing endpoints, schemas, access Strong experience in full stack fundamentals and microservices. Production experience with API authentication and authorization (OAuth 2.0, OpenID Connect, and SAML) is required.





Deep understanding of structured and unstructured data management and their corresponding technologies. Proven experience in automated testing, including unit and functional testing, and the ability to develop test strategies and design automation frameworks.

Preferred Qualifications: Experience with Agentic AI frameworks and designing multi-step AI reasoning processes Experience with MLOps principles and tools for model versioning (e.g., Git), containerization (e.g., Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., classification, regression, clustering, dimensionality reduction) and their applications in areas such as 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 Is this role right for you? In this role, you will: Deliver and Scale Agentic AI Solutions: Design, build, and productionize LLM-powered and agentic applications, including retrievalaugmented generation (RAG), multi-step reasoning workflows, structured outputs, and prompt safety. Build and consume MCP servers, defining schemas, endpoints, and access boundaries that enable safe, scalable tool use.

Work hands-on to de-risk complex problems by writing, reviewing, and operating production-grade AI systems.

Architect

Secure, Reliable, and Observable Systems: Partner closely with product, data, and engineering stakeholders to deliver AI capabilities that drive tangible outcomes. Apply strong fundamentals in structured and unstructured data, distributed systems, and service integration. Ensure systems are testable, observable, and resilient, with automated testing and explicit operational feedback loops.

Design and operate secure, low-latency services and microservices with modern authentication and authorization. Contribute to architectural discussions,



platform capabilities, and evolving best practices for AI development. Collaborate with platform and security partners to ensure systems meet enterprise risk, compliance, and operational standards Influence Technical Direction and Engineering Culture Take ambiguous problems and translate them into clear technical solutions, communicating trade-offs and constraints.

Model a culture of engineering excellence, inclusion, and continuous learning — digging into root causes and sharing durable lessons. Mentor peers through code reviews and design discussions, raising the bar for quality, ownership, and long-term thinking.

Required Qualifications: Extensive experience in Python and its core data science libraries (e.g., Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn). Hands-on experience building LLM-powered applications — retrieval, agents, structured outputs, prompt safety. Hands-on experience building and consuming MCP servers (designing endpoints, schemas, access boundaries) Strong experience in full stack fundamentals and microservices.

Production experience with API authentication and authorization (OAuth 2.0, OpenID Connect, and SAML) is required. Deep understanding of structured and unstructured data management and their corresponding technologies. Proven experience in automated testing, including unit and functional testing, and the ability to develop test strategies and design automation frameworks.

Preferred Qualifications: Experience with Agentic AI frameworks and designing multi-step AI reasoning processes Experience with MLOps principles and tools for model versioning (e.g., Git), containerization (e.g., Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., classification, regression, clustering, dimensionality reduction) and their applications in areas such as 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 modern web component frameworks (such as React & Angular Disclaimer:

AI may be used in evaluating candidates.

This posting is for an existing vacancy.

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