Backend AI Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, develop, and maintain backend services and APIs that support GenAI, LLM, and Computer Vision model integrations.

• Build and manage AI/ML infrastructure, which includes model-serving pipelines, inference services, data pipelines, and embedding/vector storage.

• Architect scalable, production-quality systems for both real-time and batch AI workloads.

• Implement and enhance RAG systems, prompt/context pipelines, and orchestration layers.

• Develop APIs, microservices, and integration layers that connect AI systems with customer data, legacy systems, and existing infrastructure.

• Ensure reliability, performance, and observability of backend AI systems, incorporating logging, monitoring, testing, and CI/CD practices.

• Collaborate with Forward Deployed Engineers, ML engineers, and product teams to convert requirements into reusable backend capabilities.

• Assess and integrate ML, CV, and LLM models into production systems; oversee model versioning, rollout, and deployment processes.

• Create architecture diagrams, API specifications, and runbooks.

• Mentor engineers and contribute to best practices in backend engineering.

⛳️ Requirements

• 4–8+ years of experience in backend software engineering, ML/platform engineering, or similar roles.

• Strong expertise in TypeScript/Node.js.

• Solid skills in API and microservice design.

• Proficiency in Python is advantageous for ML/model integration tasks.

• Practical experience in building and managing production backend systems at scale.

• Familiarity with distributed systems, databases, and message queues.

• Experience in integrating ML or Generative AI models, such as LLMs and multimodal models, into backend services.

• Knowledge of inference, orchestration, and model evaluation.

• Understanding of AWS, GCP, or Azure platforms.

• Experience with Docker and Kubernetes.

• Background in designing and managing batch and/or streaming data pipelines.

• Hands-on experience in constructing RAG systems and AI memory architectures.

• Experience with retrieval pipelines, vector storage, context management, and long-term/session memory for LLM applications.

• Strong grasp of scalability, reliability, security, and observability principles.

• Comfortable collaborating with cross-functional teams including ML engineers, product managers, and customer-facing teams.

• Bachelor's degree or higher in Computer Science or a related discipline.

• Preferred: familiarity with PyTorch, TensorFlow, or OpenCV.

• Preferred: experience with MLOps tools, model registries, feature stores, CI/CD for ML, and ML monitoring/observability.

• Preferred: background in Kafka, gRPC, WebSockets, industrial, IoT, or operational technology environments.

• Preferred: experience in startup or high-growth settings.

🏝️ Benefits

• Equity package.

• Significant opportunities for career development and advancement.

• Comprehensive salary and equity package.

• Innovative work environment focused on AI and automation technologies.

• Collaborative company culture.

• Continuous improvement opportunities.