Verification engineer Jobs in Toronto, ON

Putting people first, every day

BDO is a firm built on a foundation of positive relationships with our people and our clients. Each day, our professionals provide exceptional service, helping clients with advice and insight they can trust. In turn, we offer an award-winning environment that fosters a with a high priority on your personal and professional growth.

Your Opportunity

As an experienced AI Engineer, you will design, build, and deploy production‑grade AI solutions that bridge experimental machine learning with scalable software engineering. In this replacement role, you will play a critical role in enabling enterprise‑ready AI capabilities with a strong focus on large language models (LLMs), retrieval‑augmented generation (RAG), and agentic workflows, operating within established governance frameworks.Responsibilities:

• Design, build, and deploy robust, scalable, production‑grade AI applications using frameworks such as LangChain, LlamaIndex, AutoGPT, and related LLM orchestration tools.
• Develop, refine, and optimize complex prompt strategies; manage model context windows; and fine‑tune models where required to maximize performance, accuracy, and cost efficiency.
• Integrate AI capabilities into existing enterprise environments through RESTful APIs, microservices, and cloud‑native architectures.
• Build, maintain, and optimize vector databases (e.g., Pinecone, Milvus, Weaviate) and design efficient data ingestion and embedding pipelines to support retrieval‑augmented generation (RAG) solutions.
• Monitor AI systems in production and proactively address issues related to hallucinations, latency, reliability, scalability, and token‑cost optimization.
• Collaborate closely with AI Architects, AI Studio Leads, ML Engineers, Data Scientists, Full‑Stack Developers, Service Line Labs, and Citizen Developers on firm‑wide initiatives and internal platforms.
• Support AI system documentation, lifecycle management, and control processes in alignment with ISO/IEC 42001 enterprise governance requirements.
• Adhere to established AI risk management, data governance, and security policies, and assist with model inventories, traceability, and change‑management activities.
• Participate in model testing and validation activities in accordance with the NIST AI Risk Management Framework, including mapping and measuring model risks.
• Support the implementation of risk‑mitigation controls and ongoing monitoring, and follow governance processes that promote transparency, accountability, and responsible AI use.

How do we define success for your role?

• You demonstrate BDO's core values through all aspect of your work: Integrity, Respect and Collaboration

• You understand your client’s industry, challenges, and opportunities; clients describe you as positive, professional, and delivering high quality work

• You identify, recommend, and are focused on effective service delivery to your clients

• You share in an inclusive and engaging work environment that develops, retains & attracts talent

• You actively participate in the adoption of digital tools and strategies to drive an innovative workplace

• You grow your expertise through learning and professional development.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with 3–5 years of professional experience in AI, machine learning, or applied software engineering.
• Expert‑level proficiency in Python, with working familiarity in Java and/or TypeScript for enterprise application development.
• Deep hands‑on experience with leading AI and LLM frameworks, including OpenAI APIs, Anthropic, Hugging Face, and LangGraph, along with a strong understanding of LLM‑based application design.
• Proven experience designing and implementing retrieval‑augmented generation (RAG) and agentic AI systems, supported by a solid grasp of scalable, production‑grade AI architectures.
• Experience working with vector databases as well as SQL and NoSQL data stores, and hands‑on exposure to cloud platforms such as Azure AI / AI Foundry, AWS Bedrock, or Google Cloud Platform (GCP).
• Practical experience with DevOps and MLOps practices, including Docker, Kubernetes, and CI/CD pipelines for machine learning workloads.
• Familiarity with machine learning lifecycle and experiment‑tracking tools such as MLflow or Weights & Biases.