Senior Software Engineer (AI & Full Stack)

Fable

2d•Remote

About The Position

Fable is seeking a Senior Software Engineer with deep backend expertise, strong full-stack development skills, and hands-on experience building AI-powered applications and agentic workflows. This senior individual contributor role involves solving complex technical problems, taking ownership from idea through production, and delivering high-quality software at speed. The engineer will report to the Director of Engineering and collaborate with Product Management, Design, Development, Quality Engineering, and Site Reliability Engineering to design, build, and scale customer-facing products and platform capabilities. The role owns technical solutions end-to-end, including architecture, implementation, testing, deployment, and support. It combines building scalable backend services and modern full-stack applications with developing AI capabilities such as agents, LLM integrations, prompt orchestration, tool calling, RAG, and semantic search. The engineer will also partner with product and design teams, champion engineering quality and operational excellence, and provide technical leadership and mentorship. Fable is a remote-first company. The role requires periodic in-person team collaboration at the Toronto office; preference is given to candidates in the Greater Toronto Area, though candidates outside the GTA may be considered if they can travel for meetings. Fable describes itself as a mission-driven environment focused on building a more inclusive digital world.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
8+ years of professional software development experience.
Extensive backend engineering experience building scalable production systems.
Strong full-stack development experience.
Experience designing and developing RESTful APIs and distributed systems.
Strong experience with cloud platforms; AWS is preferred.
Experience with modern databases, caching technologies, and messaging systems.
Strong understanding of software architecture, design patterns, and engineering best practices.
Experience working in Agile software development environments.
Excellent analytical, problem-solving, and communication skills.
Hands-on experience building AI-enabled applications and developing AI agents and agentic workflows.
Experience integrating large language models, prompt engineering and optimization, tool calling, AI orchestration, AI evaluation and testing, rapid prototyping, and AI-assisted software development.
Technical skills in one or more of TypeScript, JavaScript, Python, Java, or C#; React or modern frontend frameworks; Node.js and backend frameworks; REST APIs and GraphQL; AWS cloud services; Docker and Kubernetes; MongoDB, PostgreSQL, and Redis; GitHub Actions or CI/CD platforms; Infrastructure as Code; observability and monitoring platforms; event-driven architectures; and serverless computing.
Demonstrated ability to take ownership and accountability, deliver with urgency while maintaining engineering standards, make informed decisions independently, adapt to changing priorities, solve complex problems pragmatically, communicate with technical and non-technical stakeholders, mentor others, and continuously learn—particularly about AI.

Nice To Haves

Experience with OpenAI, Anthropic Claude, Model Context Protocol (MCP), LangChain, vector databases, or similar frameworks is considered a strong asset.
AWS is the preferred cloud platform.
Candidates based in the Greater Toronto Area are preferred.

Responsibilities

Own technical solutions end-to-end, translating customer and business needs into scalable, production-ready software.
Design, develop, test, deploy, and support software throughout its lifecycle.
Take accountability for solution quality, maintainability, security, scalability, and operational excellence.
Make architectural and implementation decisions with minimal oversight; drive execution while balancing speed and engineering quality.
Identify risks, remove blockers, and improve engineering effectiveness.
Design and build scalable backend services, APIs, microservices, and distributed systems.
Develop full-stack applications across backend and frontend technologies.
Write clean, maintainable, well-tested, and secure code; optimize performance, scalability, observability, and reliability.
Contribute to architecture, technical design, and platform evolution.
Improve developer experience through automation, tooling, and reusable engineering patterns.
Build AI-powered product capabilities, agents, agentic workflows, and intelligent automation solutions.
Integrate LLMs, AI services, and external APIs into production applications; build prompt orchestration, tool-calling, and multi-agent workflows.
Implement RAG, semantic search, and knowledge retrieval where appropriate.
Evaluate emerging AI technologies, rapidly prototype capabilities, and use AI-assisted development tools to improve productivity.
Identify opportunities to use AI to improve engineering workflows and customer experiences.
Partner with Product Managers and Designers to understand customer problems and business objectives, and translate them into software solutions.
Participate in technical discovery, solution design, estimation, and delivery planning.
Build solutions that prioritize accessibility, usability, reliability, and customer outcomes.
Champion code quality, automated testing, CI/CD, observability, and secure software development.
Participate in peer code reviews, architectural design reviews, production support, incident response, and root cause analysis.
Improve engineering standards through documentation, automation, and continuous improvement; reduce technical debt while maintaining delivery momentum.
Provide technical leadership, mentor engineers, share knowledge, and influence technical direction through collaboration and sound engineering judgment.
Foster ownership, accountability, innovation, and continuous improvement.
Within the first year, independently deliver complex solutions from design through production; build AI-powered capabilities; contribute to platform architecture and engineering standards; improve quality and operational excellence; accelerate execution through AI-assisted development and automation; and serve as a trusted technical leader.