Join our Team

At this time, Ericsson Canada Inc. does not provide immigration assistance/sponsorship now or in the future for this position.

About the Opportunity

At Ericsson, we are transforming how large-scale software systems are understood, designed, developed, tested, and maintained through Generative AI and agentic software engineering.

We are seeking an Applied AI Software Designer with 2–4 years of software engineering experience and hands‑on experience applying AI to developer workflows. You will use Large Language Models (LLMs), AI agents, knowledge systems, and software‑analysis tools to enable agentic design and development across large, complex existing codebases.

You will build practical AI‑driven engineering solutions, reusable tools, and knowledge foundations that improve developer productivity, software quality, and engineering efficiency across Ericsson's 5G/6G systems. Working closely with software developers, architects, AI specialists, and domain experts, you will help turn emerging AI capabilities into reliable, secure, and reusable engineering solutions.

What you will do:

Agentic Software Engineering & Code Understanding

- Design and develop agentic workflows covering feature design, code implementation, debugging, test generation, code review, refactoring, documentation, and impact analysis.
- Build RAG, context engineering, and knowledge engineering solutions that provide agents with relevant source code, architecture, requirements, documentation, and domain knowledge.
- Combine LLM‑based reasoning with deterministic software analysis, including code indexing, dependency analysis, call relationships, static analysis, and code graphs.
- Integrate coding agents with source control, CI/CD pipelines,



testing environments, APIs, and engineering knowledge sources using tool calling, MCP, and specialized agents where appropriate.
- Develop validation approaches for AI‑generated code and engineering outputs, addressing correctness, grounding, traceability, and reliability.
- Identify high‑value engineering use cases, prototype solutions, evaluate their impact on development efficiency and quality, and productionize successful approaches.
- Measure the usability, adoption, and impact of these solutions on development efficiency and software quality.

AI Developer Tools and Reusable Engineering Foundations

- Develop reusable AI tools, components, agent workflows, and integration patterns that can be adopted across multiple engineering teams.
- Integrate AI capabilities into developer environments, coding assistants, repositories, and existing engineering workflows.
- Contribute to the development of AI‑enabled developer platforms, tool registries, knowledge services, and agentic development environments.
- Identify opportunities to improve software reuse, reduce duplicated engineering effort, and standardize effective AI‑assisted development practices.
- Apply these capabilities to large‑scale C/C++ and Python systems, considering complex architectures, interfaces, dependencies, and cross‑component interactions.

Quality,



Security and Responsible AI Engineering

- Develop repeatable benchmarks and evaluation methods to assess AI agent accuracy, reliability, and effectiveness across representative engineering tasks.
- Analyze agent failures and knowledge gaps, and improve context, retrieval, tools, and workflows through iterative experimentation.
- Integrate appropriate security, access‑control, data‑protection, intellectual‑property, and compliance considerations into AI‑enabled engineering workflows.
- Apply software quality practices, automated testing, and CI/CD integration to validate AI‑generated outputs and maintain reliable engineering solutions.
- Contribute to reusable standards and guardrails that support protected, consistent, and responsible AI adoption.

Collaboration, Adoption and Continuous Improvement

- Collaborate with developers, architects, AI specialists, and domain experts to identify high‑value use cases and translate engineering needs into practical solutions.
- Evaluate and measure the impact of AI‑driven workflows on development productivity, software quality, engineering effort, and developer experience.
- Gather feedback from users and continuously improve the usability, reliability, and adoption of AI‑enabled tools.
- Contribute to the design and development of AI‑native applications for 5G/6G systems.

The skills you bring:

- Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical Engineering, AI/ML, or a related technical field.
- 2–5 years of software engineering experience developing embedded software for wireless Layer 2 (L2) protocol stacks in 3GPP‑based cellular systems, particularly

📌 APPLIED AI SOFTWARE DESIGNER (Canada)
🏢 Ericsson
📍 Canada