Sr. AI Engineer
- Role
- AI / ML
- Experience
- Senior
Open to CA only. Set where you work from to check your eligibility.
No BS summary
Senior AI engineer with strong Python backend experience and applied AI/ML production skills. Must know FastAPI or Flask, background processing, LLM integration, RAG/vector databases, performance tuning, testing, and debugging production systems. Remote Canada.
Core skills
Required skills
Optional skills
About Us We’re a fast-growing product company integrating cutting-edge AI capabilities into our core offering to stay competitive and deliver exceptional value to customers. Our AI work spans task-specific ML models, large language model (LLM) integration, and agentic systems that orchestrate multiple tools to produce end-user results. We run a Python-based backend (FastAPI + Gunicorn + Nginx) with heavy background job processing using Celery. We’re looking for a senior-level AI Engineer who is equally strong in backend engineering and applied AI — capable of building production-grade systems that are fast, reliable, and maintainable. What You’ll Do Design, develop, and deploy production-grade AI-powered backend systems. Integrate LLMs and traditional ML models into performant, scalable architectures. Integrate and optimize vector databases for retrieval-augmented generation (RAG) pipelines and other traditional ML queries. Write clean, well-structured, and testable Python code following best practices. Capable of thinking about performance and ensuring optimal decision making to reduce latency. Build hybrid architectures that balance LLM calls with traditional ML. Debug complex, cross-layer issues spanning backend, AI inference, and UI integration. Conduct thorough dev testing before QA handoff to ensure production reliability. Collaborate with product, backend, and frontend engineers to deliver cohesive solutions. Must-Have Skills & Experience 3–5+ years professional backend engineering experience in Python, FastAPI or Flask, and background processing. Proven record of deploying Python applications to production (not just scripts or academic work). Strong grasp of software design patterns Strong understanding of backend performance, parallel processing in background jobs and multi-threading Proficiency in performance tuning specially for heavy AI models Applied machine learning experience — training, evaluating, and maintaining small task-specific models. Familiarity with LLM integration, prompt engineering, and context window optimization. Proven ability to debug AI behavior, identify root causes, and make targeted fixes. Strong testing discipline for both backend and AI components. Experience with background processing with Celery or other major libraries Experience with monitoring APIs and background processing Experience with ensuring visibility and error reporting. Nice to have: experience with Docker, understanding of CI/D, deployment automation and Kubernetes Who Will Succeed in This Role Independent problem solver — you can debug without constant supervision. Production mindset — you understand that reliability, scalability, and maintainability matter as much as accuracy. System thinker — you see backend, AI, and UI as a connected whole. Why Join Us Direct impact on the company’s competitive edge. Small, fast-moving team with high autonomy. Work on practical, real-world AI applications — not just research. Opportunity to shape our AI architecture and best practices from the ground up. If you’re a backend-first AI engineer who thrives in shipping production-ready systems and knows how to make AI practical, fast, and reliable — we’d love to talk.
What you'll do
- Design, develop, and deploy production-grade AI-powered backend systems.
- Integrate LLMs and traditional ML models into performant, scalable architectures.
- Integrate and optimize vector databases for retrieval-augmented generation (RAG) pipelines and other traditional ML queries.
- Write clean, well-structured, and testable Python code following best practices.
- Capable of thinking about performance and ensuring optimal decision making to reduce latency.
- Build hybrid architectures that balance LLM calls with traditional ML.
- Debug complex, cross-layer issues spanning backend, AI inference, and UI integration.
- Conduct thorough dev testing before QA handoff to ensure production reliability.
- Collaborate with product, backend, and frontend engineers to deliver cohesive solutions.
What they require
- 3–5+ years professional backend engineering experience in Python, FastAPI or Flask, and background processing.
- Proven record of deploying Python applications to production (not just scripts or academic work).
- Strong grasp of software design patterns.
- Strong understanding of backend performance, parallel processing in background jobs and multi-threading.
- Proficiency in performance tuning specially for heavy AI models.
- Applied machine learning experience — training, evaluating, and maintaining small task-specific models.
- Familiarity with LLM integration, prompt engineering, and context window optimization.
- Proven ability to debug AI behavior, identify root causes, and make targeted fixes.
- Strong testing discipline for both backend and AI components.
- Experience with background processing with Celery or other major libraries.
- Experience with monitoring APIs and background processing.
- Experience with ensuring visibility and error reporting.
- Independent problem solver — you can debug without constant supervision.
- Production mindset — you understand that reliability, scalability, and maintainability matter as much as accuracy.
- System thinker — you see backend, AI, and UI as a connected whole.
- Preferred: experience with Docker, understanding of CI/D, deployment automation and Kubernetes.
Benefits
- Direct impact on the company’s competitive edge.
- Small, fast-moving team with high autonomy.
- Work on practical, real-world AI applications — not just research.
- Opportunity to shape our AI architecture and best practices from the ground up.
A fast-growing product company integrating AI capabilities into its core offering; AI work spans task-specific ML models, LLM integration, and agentic systems.