About The RoleFDM is seeking a
Experienced AI Developer located in
Toronto to support a project in the
Financial sector. Involvement in this project is anticipated to last initially
12 months but may be extended.This role will be
hybrid with requirements to be in office
3 days per week.
We are looking for hands-on engineers who can turn business requirements into reliable AI applications. Potential projects include enterprise knowledge assistants, intelligent document processing, agentic workflows, and AI-enabled software development.
What you'll do - Build and integrate AI capabilities into enterprise applications and business workflows.
- Develop backend services, APIs, and data integrations supporting AI solutions.
- Implement retrieval-augmented generation (RAG), LLM integrations, or agentic workflows, depending on project requirements.
- Evaluate solution quality, accuracy, latency, and cost, and implement appropriate monitoring and controls.
- Collaborate with business stakeholders, data engineers, architects, security, and platform teams.
- Support testing, cloud deployment, production troubleshooting, and ongoing improvement.
About YouWhat you'll bring - 5+ years of software development or related engineering experience, including hands-on delivery of AI-enabled applications.
- Strong Python development skills and experience building maintainable, tested software.
- Practical experience with LLM APIs, generative AI applications, or production machine-learning systems.
- Experience with REST APIs, SQL, data integration, and backend development.
- Hands-on experience with at least one cloud platform, preferably AWS or Azure.
- Familiarity with Git, automated testing, CI/CD, and containerized deployment.
- Strong communication skills and experience delivering within cross-functional teams.
Additional experience we value - RAG and enterprise search: document ingestion, embeddings, vector databases, retrieval, and evaluation.
- Agentic applications: tool integration, workflow orchestration, human approvals, and reliable execution.
- AI-enabled engineering: developer tooling, automated testing, code analysis, and SDLC automation.
- Production AI: evaluation datasets, observability, access controls, sensitive-data handling, and model lifecycle management.
- Relevant technologies: LangChain, LangGraph, Azure OpenAI, Amazon Bedrock, or comparable tools.
- Financial services: banking, capital markets, insurance, or other regulated enterprise environments.
Candidates are not expected to know every framework listed. We value strong engineering fundamentals and clear evidence of delivering working AI solutions.
