AI Engineer

About Optimize Financial Group: Optimize Financial Group is a premier wealth management firm based in Toronto, ON, dedicated to helping clients achieve their long-term financial goals. We provide personalized financial solutions spanning portfolio management, tax planning, debt management, retirement planning, and estate strategies. Our holistic approach aligns financial objectives with life ambitions, ensuring every client receives a thoughtful and tailored path to success.

The opportunity: We are seeking a forward-thinking AI Engineer to join our growing team. In this role, you will design, develop, and optimize AI-driven solutions that enhance efficiency, drive innovation, and support business growth across our advisory, operations, and client service functions. You will be responsible for building and refining intelligent systems, integrating AI tools and APIs, and collaborating with cross-functional teams to transform cutting-edge technologies into practical, scalable business applications.

This is a full-time, on-site position (Monday to Friday, 8:30 a.m. to 5:30 p.m.) at our downtown Toronto office. Key responsibilities: Design, build, test, deploy, and maintain LLM-based applications, prompt architectures, AI workflows, and automation tools that support client engagement, advisor support, operations, data analysis, and internal business processes. Partner with internal stakeholders to identify, scope, prioritize, and deliver high-impact AI use cases, balancing business value, technical feasibility, implementation complexity, risk, and cost.

Integrate AI-powered applications, APIs, automation workflows, and internal data sources into existing business systems, ensuring solutions are reliable, scalable, maintainable, and aligned with business needs. Develop and maintain a centralized AI knowledge and implementation library, including tested prompts, reusable workflows, model configurations, evaluation examples, integration patterns, documentation, and best practices. Create structured evaluation processes for AI solutions, including test cases, expected outputs, quality criteria, failure analysis, human-in-the-loop review, and ongoing performance monitoring.

Support responsible AI implementation by helping define and apply best practices for privacy, data handling, output validation, documentation, auditability, and appropriate human oversight. Monitor emerging AI models, tools, vendors, frameworks, and implementation patterns to assess practical business applications and recommend where new capabilities can create measurable value. Support internal AI enablement by educating teams on effective AI usage, prompt design, workflow automation, responsible implementation, and the practical limitations of AI systems.

Qualifications : Minimum of 2 years of relevant technical experience in software engineering, AI engineering, automation, data products, or technical business solutions, with hands-on experience building or integrating generative AI tools, APIs, or platforms. Strong software engineering fundamentals, including experience designing, testing, deploying, debugging, documenting, and maintaining technical solutions in a production or business environment. Proficiency with Python, JavaScript, TypeScript, or similar programming languages used to build, test, and integrate AI-enabled applications and automation workflows.

Experience working with LLM APIs, AI platforms, or generative AI development tools such as the OpenAI API, Anthropic Claude, Google Gemini, Azure OpenAI, Microsoft Copilot Studio, or similar technologies. Experience using Git and platforms such as GitHub, GitLab, or Bitbucket for version control, collaboration, code review, and deployment workflows. Strong understanding of LLM behavior, prompt optimization, context management, model limitations, hallucination risks, output validation, and applied AI design patterns.

Experience integrating APIs, automation tools, databases, internal documents, and cloud-based services such as AWS, Azure, or Google Cloud into business workflows or internal applications. Experience evaluating AI outputs using structured test cases, quality criteria, benchmark examples, human review workflows, failure analysis, or performance monitoring. Understanding of responsible AI practices, including privacy, data security, human oversight, documentation, auditability, and appropriate handling of client or business data.

Strong communication skills, with the ability to translate complex AI and technical concepts into clear, practical recommendations for non-technical stakeholders. Demonstrated ability to build or implement AI-enabled solutions that improve real-world business outcomes, such as efficiency, accuracy, client experience, scalability, process consistency, or decision support.