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 protected,
- 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
- Solid 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 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,
- 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.

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📌 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. (Toronto)
🏢 S.i. Systems
📍 Toronto