Lead AI Engineer (LLM & Platform) - Remote Canada

Job Title: Lead AI Engineer (LLM & Platform)

Location: 100% remote anywhere in Canada

Duration: Full-time

Job Description:

Role Overview

We are seeking a highly experienced Lead AI Engineer to design, build, and scale AI-powered features used in production environments. This role is hands-on and execution-focused, combining deep AI/ML expertise with strong software engineering fundamentals. You will play a key role in shaping our AI architecture, validating AI capabilities through experimentation, and delivering reliable, high-quality AI systems at scale.

Key Responsibilities

• Design, develop, and deploy production-grade AI/ML features, with a strong focus on LLM-powered systems.
• Architect and implement RAG pipelines, agentic workflows, and AI services using vector databases such as Databricks, pgVector, or equivalent.
• Build scalable Python-based microservices using frameworks like FastAPI, Flask, or Django.
• Collaborate with product, data, and engineering teams to translate business problems into effective AI solutions.
• Define and execute experimentation strategies, including A/B testing, evaluation frameworks, and hypothesis-driven validation of AI features.
• Ensure high code quality and system reliability through testing, monitoring, observability, and performance optimization.
• Contribute to cloud-native architectures leveraging AWS, GCP, or Azure, with containerization (Docker/Kubernetes) and CI/CD pipelines.
• Mentor engineers and influence best practices in AI engineering, architecture, and delivery.

Core Technical Skills

• 10+ years of software engineering experience, with significant hands-on focus on AI/ML systems in production.
• Deep expertise in LLMs, including prompt engineering, RAG architectures, and agentic AI frameworks (e.g., LangChain, Google ADK, or similar).
• Strong proficiency in Python and modern backend frameworks.
• Solid foundation in statistical methods, data processing, ETL pipelines, and large-scale data workflows.
• Experience with cloud platforms, container orchestration, and modern DevOps practices.
• Strong understanding of AI system observability, monitoring, testing, and operational excellence.

Ways You’ll Stand Out (Nice to Have)

• Experience with model fine-tuning, RLHF, or custom model training.
• Familiarity with MLOps tools and metrics (e.g., MLflow, precision/recall, model evaluation frameworks).
• Knowledge of frontend technologies (React) for full-stack contributions.
• Background in NLP, multimodal AI, document processing, or real-time / streaming AI systems.
• Open-source contributions, technical writing, or public speaking experience.