Posted 17 days ago
AI Developer | Applied AI & Intelligent Automation
@ FDM Group
Toronto, Ontario
Responsibilities:
- 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.
Requirements summary:
- This role will be hybrid with requirements to be in office 3 days per week.
- Potential projects include enterprise knowledge assistants, intelligent document processing, agentic workflows, and AI-enabled software development.
- Implement retrieval-augmented generation (RAG), LLM integrations, or agentic workflows, depending on project requirements.
- What 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.
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-10-27
- First seen by our source
- 2026-11-14
- Source record updated
- 2026-11-14
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
FDM Group
| Field | Value |
|---|---|
| Open jobs on Jobsy | 4 |
About The Role
FDM is a global business and technology consultancy delivering client and industry driven solutions through our 5 core specialist Practices; Software Engineering, Data & Analytics, IT Operations, Change & Transformation, and Risk, Regulation & Compliance.
FDM 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 You
What 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.
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