Closed listing
Senior AI Engineer
- Toronto, ON, Canada
$60 to $75 CAD / hour · Contract
This opening has closed.
The original listing is kept below for reference.
Job details
- Pay
- $60 to $75 CAD / hour
- Job type
- Contract
- Closing date
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Senior AI Engineer – Agentic AI & AWS Bedrock
Engagement: Contract
Work Model: Hybrid / Flexible Location
Project: Enterprise Regulatory Compliance AI Platform
Overview
We are seeking an experienced senior AI engineer** to support the development of an enterprise-grade regulatory compliance platform using agentic AI.
The solution will use intelligent agents to help organizations manage business compliance, maintain ongoing adherence to regulatory requirements, and identify potential compliance drift over time.
The successful candidate will work closely with business and regulatory subject matter experts while taking a hands-on role in the AI architecture, modeling, evaluation and implementation of the platform.
Key Responsibilities
We are looking for a hands-on senior AI engineer rather than a purely advisory architect. The ideal candidate can design the solution architecture while also building, testing and evaluating production-grade AI agents.
The role will operate as part of an integrated client/partner team, with regulatory subject matter expertise provided by the client and the AI engineering team responsible for translating that expertise into reliable, scalable AI capabilities.
Engagement: Contract
Work Model: Hybrid / Flexible Location
Project: Enterprise Regulatory Compliance AI Platform
Overview
We are seeking an experienced senior AI engineer** to support the development of an enterprise-grade regulatory compliance platform using agentic AI.
The solution will use intelligent agents to help organizations manage business compliance, maintain ongoing adherence to regulatory requirements, and identify potential compliance drift over time.
The successful candidate will work closely with business and regulatory subject matter experts while taking a hands-on role in the AI architecture, modeling, evaluation and implementation of the platform.
Key Responsibilities
- Design and develop AI agents and multi-agent workflows for regulatory compliance use cases.
- Build AI solutions using Python and AWS Bedrock.
- Design the underlying architecture for agent orchestration, reasoning, and workflow execution.
- Develop robust AI evaluation frameworks to measure accuracy, reliability, consistency, and quality of agent outputs.
- Implement mechanisms to identify and monitor compliance drift over time.
- Collaborate with regulatory and business SMEs who will validate agent outputs and business rules.
- Develop production-quality components that can integrate into the broader DLO platform.
- Support testing, iteration and continuous improvement of AI agent performance.
- Document architecture, evaluation methodology and technical design decisions.
- Strong hands-on experience developing Generative AI / LLM applications.
- Advanced Python development skills.
- Practical experience with AWS Bedrock and AWS-based AI architectures.
- Experience designing and implementing AI agents, agentic workflows or multi-agent systems.
- Experience building LLM evaluation frameworks, including approaches for measuring output quality, hallucination, accuracy and reliability.
- Strong understanding of prompt engineering, model selection, orchestration and production AI deployment.
- Experience taking AI solutions from prototype through production implementation.
- Ability to work directly with business and domain SMEs to translate requirements into AI workflows.
- Experience with enterprise compliance, governance, risk, or regulatory applications.
- Experience with AI guardrails, responsible AI, and model governance.
- Familiarity with RAG, knowledge bases, embeddings, and enterprise data integration.
- Experience monitoring AI systems for performance degradation or drift.
- Previous consulting, enterprise or professional-services experience.
We are looking for a hands-on senior AI engineer rather than a purely advisory architect. The ideal candidate can design the solution architecture while also building, testing and evaluating production-grade AI agents.
The role will operate as part of an integrated client/partner team, with regulatory subject matter expertise provided by the client and the AI engineering team responsible for translating that expertise into reliable, scalable AI capabilities.
More about this job
Responsibilities
Design, build, and evaluate production-grade AI agents and multi-agent workflows for an enterprise regulatory compliance platform using Python and AWS Bedrock. Collaborate with business and regulatory experts to implement compliance monitoring and drift detection, integrate components into the broader platform, and document architecture and evaluation decisions.
Requirements
Requires advanced Python skills and hands-on experience developing and deploying generative AI and LLM applications, including practical AWS Bedrock experience. Candidates should have experience with agentic workflows, LLM evaluation, prompt engineering, production AI implementation, and translating business requirements into AI workflows.
Skills
- Generative AI
- Large Language Models
- Python
- AWS Bedrock
- AWS AI Architecture
- AI Agent Development
- Multi-Agent Systems
- Agent Orchestration
- LLM Evaluation
- Prompt Engineering
- Model Selection
- AI Guardrails
- Responsible AI
- Retrieval-Augmented Generation
- Knowledge Bases
- Compliance Drift Monitoring
Visa sponsorship
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Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Consulting
Keywords
- Generative AI
- Large Language Models
- Python
- AWS Bedrock
- AWS
- AI Agents
- Agentic AI
- Multi-Agent Workflows
- Agent Orchestration
- LLM Evaluation Frameworks
- Prompt Engineering
- Model Selection
- Production AI Deployment
- Regulatory Compliance
- Compliance Drift
- AI Guardrails
- Responsible AI
- Model Governance
- Retrieval-Augmented Generation
- Knowledge Bases
- Embeddings
- Enterprise Data Integration
- AI Architecture
- Hallucination Evaluation
- Business Requirements