Lead Full-Stack GenAI Developer (Contractor)
- Toronto, ON, Canada
Full-time
Applications open in a new tab.
Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
About the Role
We are seeking a Senior Lead Full-Stack GenAI Developer for a 6-month contract engagement to lead the end-to-end hands-on build, integration, and deployment of enterprise GenAI and Agentic AI applications. Partnering closely with our Principal AI Solution Architect, you will own the full-stack development - spanning backend Python services, frontend interfaces, retrieval pipelines, AI agents, enterprise integrations, and GenAIOps.
-
Hands-on AI Implementation: Personally author and maintain production-grade Python microservices, custom APIs, retrieval (RAG) systems, and agentic workflows.
-
Full-Stack Application Building: Create reviewer workbenches, operational dashboards, and user interfaces using React / TypeScript paired with backend-for-frontend APIs.
-
Enterprise Integration: Seamlessly integrate AI services into enterprise data pipelines, APIs, databases, and platform ecosystems including Microsoft Azure, Databricks, and Microsoft Foundry.
-
Evaluation & GenAIOps: Design automated evaluation test suites (RAG metrics, model latency, cost, groundedness) and establish CI/CD pipelines, Infrastructure-as-Code (IaC), containerization, and end-to-end telemetry.
-
Reusable Capabilities & Mentorship: Standardize repeated delivery patterns into reusable SDKs, component libraries, and templates, while mentoring internal engineering team members.
Required Qualifications
-
Seniority & Experience: Proven track record as a Senior or Lead Engineer personally contributing code to production-deployed GenAI systems.
-
Backend & AI Core: Deep Python proficiency, REST/GraphQL APIs, RAG architectures, vector databases, prompt engineering, agentic frameworks, and automated LLM evaluation practices.
-
Frontend UI: Strong production skills in React and TypeScript.
-
Cloud & Platform Ecosystems: Practical experience within Microsoft Azure (Azure OpenAI, Azure AI Search) and Databricks environments.
-
Data & Ops: Proficiency in SQL, data engineering pipelines, CI/CD, Docker/containers, Infrastructure-as-Code, and runtime telemetry/monitoring.
Important Candidate Note:
Candidates focused primarily on prompt design, research notebooks, architectural oversight, project coordination, or generic web development without recent, hands-on production AI coding, enterprise integration, deployment automation, and production support will not be considered for this role.
More about this job
Responsibilities
Lead hands-on development, integration, and deployment of enterprise GenAI and agentic AI applications, including Python services, APIs, retrieval systems, workflows, and React/TypeScript interfaces. Build evaluation and GenAIOps capabilities, integrate with enterprise platforms, standardize reusable engineering components, and mentor internal engineers.
Requirements
Requires senior or lead-level experience personally coding production GenAI systems, with strong Python, API, RAG, vector database, agentic framework, and LLM evaluation expertise. Candidates must also have production React/TypeScript skills and practical experience with Azure, Databricks, SQL, data pipelines, CI/CD, containers, Infrastructure as Code, and telemetry.
Skills
- Python
- React
- TypeScript
- REST APIs
- GraphQL
- Retrieval-Augmented Generation
- Vector Databases
- Prompt Engineering
- Agentic AI Frameworks
- Microsoft Azure
- Azure OpenAI
- Azure AI Search
- Databricks
- SQL
- CI/CD
- Infrastructure as Code
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Data & Analytics
- Management & Leadership
Keywords
- Generative AI
- Agentic AI
- Python
- Microservices
- REST APIs
- GraphQL
- Retrieval-Augmented Generation
- Vector Databases
- Prompt Engineering
- LLM Evaluation
- React
- TypeScript
- Backend-for-Frontend APIs
- Microsoft Azure
- Azure OpenAI
- Azure AI Search
- Databricks
- Microsoft Foundry
- SQL
- Data Engineering Pipelines
- CI/CD
- Infrastructure as Code
- Docker
- Containerization
- Telemetry
- Monitoring
- GenAIOps
- Automated Evaluation
- Enterprise Integration
- SDKs