The Senior Applied AI Engineer is responsible for designing, developing, and deploying advanced Gen AI and machine learning solutions that drive business value in Wealth Management. This role blends deep technical expertise with financial domain knowledge, collaborating with cross‑functional teams to deliver scalable, secure, and compliant AI products. The engineer will design and implement GenAI and agent‑based systems to enhance advisor workflows, client engagement, and operational efficiency. Key Responsibilities Contribute to the overall Wealth Management AI strategy formulation and execution Architect and build GenAI-powered applications for Wealth management use cases. Liaise with relevant technology teams to build and deploy Gen AI solutions Lead solution development, from ideation to prototyping, and liaise with partners to facilitate production and monitoring. Collaborate with product managers, data scientists, engineers, and stakeholders to translate requirements into technical solutions. Integrate AI solutions with existing platforms (CRM, portfolio management, data warehouses) and ensure interoperability. In collaboration with risk and RAIOps teams, ensure all AI solutions meet performance, security, and regulatory standards. Leverage best practices in MLOps, model governance, and responsible AI. Stay current with industry trends and emerging AI technologies in financial services. Document technical designs, create demos, and support enablement and adoption across the organization. Required Skills and Tools Advanced proficiency in Python and machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Experience building Copilot Studio Agents. Proficiency with GenAI, LLMs (OpenAI, Google Gemini, Anthropic) and agent orchestration frameworks (LangChain, LangGraph,



AutoGen). Solid understanding of cloud platforms (Azure, AWS, GCP) and MLOps tools (MLflow, Kubeflow, Airflow). Familiarity with financial services workflows, data privacy, and compliance requirements. Hands‑on experience with AWS and Azure AI/ML, data, and deployment toolsets (SageMaker, S3, Lambda, Azure Functions, Azure ML, Blob Storage). Proficiency with Kubernetes for container orchestration and deployment of scalable AI solutions. Experience with Docker and CI/CD pipelines. Expertise in data engineering, model deployment, and monitoring. Knowledge of API integration, microservices, and scalable system architecture. Excellent problem‑solving, communication, and stakeholder management skills. Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. 10+ years of experience in AI/ML engineering, with at least 2 years in financial services or regulated industries or a combination of relevant experience and education. Proven track record of designing and deploying production‑grade AI solutions. Experience working in Agile teams and collaborating with cross‑functional stakeholders. Demonstrated ability to mentor and lead technical teams. Advanced Level Proficiency Gen AI, Mathematics, Critical thinking, Creative reasoning, Computational Thinking, Deep Learning, Machine Learning, Scaling Models, Continuous Integration and Continuous Delivery/Deployment, ML algorithms, Excellent communication skills, Analytical and problem solving, Influence skills, Collaboration and team skills, Managing ambiguity. Salary $103,200.00 – $192,000.00 (Salaried). Salaries vary by location, skills, experience, education, and qualifications and may include commission and performance‑based incentives. #J-18808-Ljbffr

📌 Senior Applied Ai Engineer - $103,200 - $192,000 A Year (Toronto)
🏢 BMO
📍 Toronto