Our client in the financial services sector is seeking a full-time, permanent Senior AI Engineer to design, develop, and deploy enterprise-grade AI and Generative AI solutions that drive business value across customer experience, operational efficiency, and digital transformation initiatives. This role will work closely with business and technology stakeholders to build scalable AI applications, agentic solutions, and machine learning platforms that support innovation while meeting enterprise security, governance, and compliance requirements.
Location
Hybrid – 3 days onsite in Toronto
Responsibilities
- Design, develop, and deploy production-grade AI, machine learning, and Generative AI solutions.
- Build AI-powered applications, copilots, and intelligent agents using enterprise AI platforms and frameworks.
- Develop and maintain AI solutions leveraging large language models (LLMs) and retrieval-based architectures.
- Design and implement agent orchestration frameworks and multi-agent workflows.
- Collaborate with business stakeholders to identify and prioritize AI use cases and opportunities.
- Develop scalable APIs, microservices, and cloud-native AI architectures.
- Deploy, monitor, and optimize machine learning models in production environments.
- Implement CI/CD pipelines, MLOps practices, and automated model lifecycle management.
- Work with cloud-based AI and data services across Azure, AWS, and/or GCP environments.
- Ensure AI solutions align with enterprise security, governance, privacy, and compliance standards.
- Mentor team members and contribute to AI engineering best practices and standards.
Requirements
- 8+ years of experience in software engineering, machine learning, data science, or AI engineering.
- Proven experience designing and deploying production-grade AI and machine learning solutions.
- Hands‑on experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit‑learn.
- Experience building Generative AI solutions using large language models such as OpenAI, Anthropic, Gemini, or similar platforms.
- Experience designing and developing AI agents using Microsoft Copilot Studio, Azure AI Foundry, or comparable enterprise AI platforms.
- Experience with agent orchestration frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.
- Strong understanding of cloud-native AI architectures across Azure, AWS, and/or GCP.
- Experience with AI/ML services including Azure AI, Azure Machine Learning, Amazon SageMaker, and related cloud platforms.
- Strong hands‑on experience with Azure and/or AWS data, AI, and application services.
- Experience with Kubernetes, Docker, and containerized application deployment.
- Experience implementing CI/CD pipelines and MLOps practices for AI solutions.
- Expertise in model deployment, monitoring, and operationalization.
- Knowledge of API integration, microservices architecture, and scalable distributed systems.
- Strong communication, stakeholder engagement, and problem‑solving skills.
Nice to Have
- Experience developing AI agents, copilots, or conversational AI solutions.
- Experience working within highly regulated industries such as financial services, insurance, healthcare, or public sector.
- Familiarity with enterprise data governance, privacy, security, and risk management practices.
- Experience building Retrieval‑Augmented Generation (RAG) solutions.
- Experience with vector databases and enterprise knowledge management platforms.
- Experience with Microsoft Copilot, Copilot Studio, Azure AI Foundry, or Microsoft AI ecosystem technologies.
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