We are seeking a skilled full time Gen AI Full Stack Developer to build and maintain end-to-end AI-powered applications within the enterprise AI Program. You will work across the entire stack - from developer portals and user-facing interfaces through API services to RAG pipelines, Knowledge Bots, and model integrations - delivering intelligent, governed AI experiences across the organization.
Our position require 3 to 4 in Brampton, in future in our office in Mississauga or Toronto.
Key Responsibilities- Design and develop full-stack AI applications: React/Angular frontends, Python/Node.js API services, and agentic backend services on Azure Kubernetes Service.
- Build RAG pipelines end-to-end: data ingestion, document chunking, embedding generation, vector storage, and retrieval - powering the KQA Platform and Internal Knowledge Bots.
- Implement developer portals and AI Launchpad interfaces that expose the shared AI infrastructure to software developers, data scientists, and business users.
- Integrate multi-model support across the AI model catalog: Azure OpenAI (GPT-4), Claude, Cohere AI, Meta, and Mistral AI via the AI Gateway and API Management layer.
- Develop content processing pipelines using Azure AI services: Content Understanding, Content Safety, AI Language Intelligence, Document Intelligence, and AI Vision.
- Connect to data platform backends — Databricks Unity Catalog and Snowflake Data Cloud — for structured and unstructured data retrieval within RAG pipelines.
- Build and consume governed multi-tenant REST APIs with proper RBAC, access-aware retrieval, and tenant isolation aligned with AI governance requirements.
- Support MLflow integration for experiment tracking, model registry, and observability across AI workloads/ Support Azure Machine Learning (AML) integration for experiment tracking, model registry, and observability, utilizing the Azure AI Foundry unified platform for managed AI workloads.
- Contribute to GitOps CI/CD pipelines for automated deployment of AI services, Knowledge Bots, and agent templates.
- Implement monitoring dashboards, evaluation tooling, and tracing capabilities to support platform observability and RLHF feedback loops.
Technical Skills Required- Languages & Frameworks: Python (FastAPI, LangChain, Semantic Kernel), TypeScript / JavaScript (React, Next.js, Node.js)
- Azure AI Services: AI Foundry, Copilot Studio, Azure OpenAI, AI Search, Content Understanding, Document Intelligence, AI Language, AI Vision, CosmosDB
- RAG & Embeddings: LlamaIndex / LangChain, Azure AI Search, document chunking strategies, embedding models, vector store design
- Data Platforms: Databricks (Unity Catalog, notebooks), Snowflake Data Cloud; SQL and PySpark basics
- MLOps: experiment tracking, model registry, evaluation, model lifecycle management
- DevOps: Docker, Kubernetes (AKS), Terraform, GitHub Actions / Azure DevOps CI/CD
- Security: OAuth 2.0, SSO / LDAP, RBAC, API key management, multi-tenant credential isolation
- Evaluation Frameworks - like Ragas , TruLens , PromptFlow
Qualifications- 5+ years of full-stack development experience; 2+ years building production GenAI / LLM applications.
- Hands-on experience with RAG pipelines, prompt engineering, and agent-based application patterns.
- Familiarity with multi-tenant SaaS architecture and API governance best practices.
- Experience integrating with enterprise data platforms (Databricks, Snowflake, or equivalent).
- Strong written and verbal communication skills; comfortable working in cross-functional Agile teams.
- Nice to have: Copilot Studio configuration experience; MLflow or Azure ML familiarity
The base compensation range for this role in the posted location is $70,751 to $165,984.
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees.