AI Engineer - Generative AI & Agentic Systems

Summary

Do you want to build AI that actually ships? We’re looking for an engineer who thinks in systems, writes production‑grade code, and gets excited about transforming cutting‑edge LLMs into reliable, scalable solutions.

As an AI Engineer at Bell, you’ll design and build the next generation of AI‑powered applications. This includes architecting agentic pipelines, wrapping models in robust APIs, and embedding Generative AI directly into products that support millions of customers and internal teams. You’ll collaborate closely with senior engineers and product partners to move quickly, iterate thoughtfully, and deliver solutions that perform in the real world.

We’re seeking someone who genuinely enjoys the craft — someone who tackles complex problems hands‑on, enjoys exploring new tools and techniques, and finds satisfaction in solving challenges that don’t always come with obvious answers. The AI landscape is evolving rapidly, and we value engineers who are curious, adaptable, and eager to grow alongside it. If you’ve been building with LLMs — from prompt engineering and RAG pipelines to tool‑calling agents and multi‑step orchestration — and you have the backend expertise to bring these solutions into production, this role offers the opportunity to make a real impact.

Key Responsibilities

• Design and implement multi-step AI agents using orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI, or custom builds) — systems that reason, use tools, retrieve context, and take action.
• Write clean, production-ready Python to build APIs, microservices, and backend logic that powers AI features end-to-end, from prompt to response.
• Build and maintain RESTful (and event-driven where appropriate) APIs that serve AI capabilities reliably across teams and applications.
• Rapidly prototype interactive demos using Streamlit or lightweight front-end tooling, then follow through to scalable, maintainable implementations.
• Integrate state-of-the-art models for text, image, and video (e.g., Gemini, Nano Banana, Veo) into internal tools and customer-facing products.
• Participate in system design discussions and contribute an engineering perspective on how to build things that scale.
• Partner with end users, data engineers, and product owners to deeply understand user needs and the full user journey — then translate those insights into technical solutions that actually solve the right problem.

Critical Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field — or equivalent professional experience
• 3+ years of professional software or AI engineering experience. You write production-grade code, not just research scripts.
• Strong Python skills, including experience building APIs with FastAPI or Flask. SQL proficiency is expected.
• Hands-on LLM experience: You've worked directly with LLM APIs (OpenAI, Gemini, Anthropic, etc.) and understand the practical realities — prompt design, context management, latency, cost, and failure modes.
• Agentic or RAG systems experience: You've built at least one system involving tool use, retrieval-augmented generation, multi-step reasoning, or autonomous agents — not just single-shot completions.
• Cloud platform experience: GCP preferred; AWS or Azure acceptable.
• Engineering mindset: You approach problems with rigor, break them down systematically, and know how to balance speed with quality.
  
  

Preferred Qualifications

• Experience with AI orchestration frameworks such as Google ADK (Agent Development Kit), LangChain, LlamaIndex, LangGraph, CrewAI, or similar.
• Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector) for semantic search and retrieval.
• Understanding of CI/CD pipelines, Git-based workflows, containerization (Docker), and model deployment practices.
• Exposure to event-driven architectures, async processing, or streaming responses (e.g., SSE, WebSockets).
• Experience building Generative AI applications involving media — image or video generation.
• Bilingual in French and English (an asset).