Lead AI Engineer / Technical Lead Manager
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- Toronto, ON
- Permanent
- Temps-plein
Role SummaryAs a Lead AI Engineer / Technical Lead Manager, you will head the technical direction and execution of our LLM and Agentic Systems strategy, with a specialized focus on bridging cloud intelligence and embedded execution. You will lead a high-performing squad of engineers to develop, fine-tune, and deploy state-of-the-art models that drive core product innovation. You are a "player-coach" who balances deep-dive architectural design—including model optimization for edge hardware—with people leadership, ensuring your team delivers scalable, production-grade AI solutions.
Responsibilities
- Strategic Technical Leadership: Own the roadmap for LLM integration and Agentic workflow orchestration. You will move projects from conceptual research to high-availability production environments, ensuring performance parity across cloud and embedded edge devices.
- Team Management & Mentorship: Direct responsibility for the growth of a specialized AI/ML team. You will provide technical mentorship in both high-level Generative AI and low-level model optimization (quantization, pruning) to ensure your team can deploy to any target.
- Architecting Agentic Systems: Lead the design of complex, multi-agent autonomous systems. You will oversee frameworks that allow LLMs to automate workflows reliably in both resource-constrained embedded environments and high-compute cloud environments.
- System Integration & Scalability: Partner with Platform and Hardware teams to architect the "AI/ML Backbone." You will ensure infrastructure supports low-latency on-device inference, vector database scaling, and efficient data exchange across the hardware ecosystem.
- Defining "Production-Grade": Establish rigorous standards for MLOps and TinyMLOps, including CI/CD for ML, automated evaluation frameworks (RAG metrics, hardware-specific latency benchmarking), and model health monitoring.
- Cognitive Automation Strategy: Identify high-impact opportunities for cognitive automation (e.g., local summarization, real-time reasoning) and delegate execution to maximize accuracy within the power and memory envelopes of our hardware.
- Cross-Functional Diplomacy: Act as the primary liaison between AI Engineering and Product, Legal, and Hardware stakeholders. You will translate complex ML constraints—such as SRAM limitations or NPU capabilities—into clear business trade-offs.
- Advanced Degree: Master’s or Ph.D. in Computer Science, Machine Learning, or a related field.
- Proven Leadership: 7+ years of total experience in ML/Software Engineering, with 2+ years in a Lead or Management role managing small to mid-size technical teams.
- Generative AI & Optimization Expertise: Extensive experience with LLM fine-tuning (PEFT, LoRA) and a solid understanding of model compression techniques (INT8/FP16 quantization) for deployment.
- Technical Stack: Mastery of Python and PyTorch/TensorFlow. Strong proficiency in C++ or Rust is highly preferred for developing performance-critical inference engines on embedded targets.
- Production Lifecycle: Proven track record of taking ML models from notebook to 24/7 production environments, including experience with Edge AI runtimes (e.g., TensorRT, ONNX Runtime, or TFLite).
- Strategic Mindset: Ability to balance the "bleeding edge" of research with the pragmatic needs of stable, low-power, and maintainable embedded products.