About the role

AI Developer developing impactful AI solutions for the nuclear industry. Working with Azure services to build intelligent tools and streamline workflows.

Responsibilities

Design and develop AI-powered solutions using Microsoft Azure services, including OpenAI, Cognitive Search, Vision, Document Intelligence, and Cosmos DB for vector storage.
Build and iterate on retrieval-augmented generation (RAG) pipelines and knowledge-grounded architectures, including GraphRAG and emerging techniques like OmniRAG.
Rapidly prototype AI applications tailored to specific client needs, such as document digitization, data vectorization, and content generation workflows.
Contribute to the architecture and implementation of advanced AI systems, including agent-based orchestration and integration with modern APIs and platforms.
Prepare and structure training data, including labeled inputs for both text and image-based models, with a focus on production readiness and model performance.
Evaluate and adopt new tools and frameworks based on AI/ML advancements, ensuring that implementations are stable and scalable.
Collaborate across teams to deliver high-impact, cloud-native applications using Python (especially Azure's AI SDK). Exposure to JavaScript/TypeScript or Node.js is considered an asset.
Apply familiarity with engineering documentation and visuals (e.g., diagrams, schematics) to enhance AI-driven workflows where applicable.

8–15 years of relevant experience in software or AI/ML development.
Strong hands-on experience with Microsoft Azure AI services, including OpenAI, AI Search, Vision, Document Intelligence, and Cosmos DB.
Proven ability to develop and deploy retrieval-based AI solutions using RAG pipelines, knowledge graphs, and related architectures.
Demonstrated skill in rapidly building and iterating AI prototypes based on client requirements.
Strong Python development skills, with experience using Azure’s AI SDK.
Experience designing and implementing AI solution architectures, including agent-based systems and orchestration tools.
Awareness of current and emerging AI/ML research, with the ability to assess production readiness.
Exposure to front-end or full-stack development (JavaScript/TypeScript, Node.js) is an asset.
Familiarity with engineering or technical documentation (e.g., schematics, diagrams) is considered an asset.
Ability to independently structure and label data for AI training pipelines.

Tools and support for mental wellness, professional growth, and financial security.
Health coverage, a spending account, and the freedom to rest and recharge.
Meaningful work, alignment with your values, and time to refresh with time off.
Competitive pay and recognition for the impact you make.