Lead Machine Learning Engineer

Lead Machine Learning Engineer – LLM Focus Location: Toronto Salary: $140,000 – $160,000 base 15% Bonus Company Overview: Join a fast-growing, innovative organization at the forefront of artificial intelligence, committed to pushing boundaries in generative AI and Large Language Models (LLMs). They are solving cutting-edge problems with scalable machine learning and deploying solutions across industries. Role Overview: As a Senior Machine Learning Engineer with a specialization in LLMs, you will play a pivotal role in architecting, building, and deploying high-impact machine learning systems. You’ll collaborate cross-functionally with data scientists, research engineers, and business stakeholders to drive real-world applications of advanced AI. Key Responsibilities: Lead development of ML/LLM solutions for tasks like summarization, classification, Q&A, and RAG. Collaborate on transformer models (e.g., GPT, LLaMA, Claude, Mistral). Fine-tune and optimize pre-trained LLMs using best practices. Build and maintain ML pipelines with MLFlow, Airflow, or Kubeflow. Partner with MLOps/DevOps to ensure scalable, secure production systems. Deploy models using Docker, Kubernetes, and serving frameworks (e.g., TensorFlow Serving, TorchServe, FastAPI). Implement model versioning, blue-green/canary deployments, and performance monitoring. Develop scalable data pipelines for text and embeddings. Stay up to date with LLM/AI research and apply findings to real-world problems. Document workflows and support knowledge sharing across the team. Lead and mentor a team of ML engineers and researchers to deliver high-impact solutions. Required Qualifications: MSc or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or related STEM field. Proven experience with LLMs and transformer-based architectures (e.g., BERT, RoBERTa, GPT, T5). Expertise in developing and deploying ML models in production environments. Strong Python programming skills; familiarity with ML/AI libraries (Hugging Face Transformers, TensorFlow, PyTorch). Experience with cloud platforms (AWS preferred), container orchestration (Kubernetes), and distributed data processing (Apache Spark, Kafka). Hands-on experience with ML tools including MLFlow, Airflow, and experiment tracking systems. Solid understanding of DevOps and CI/CD pipelines for ML systems. Strong communication skills with the ability to articulate technical details to non-technical stakeholders. Preferred Experience: Experience in retrieval-augmented generation (RAG), vector databases (e.g., Pinecone, FAISS, Weaviate), and embedding models. Exposure to open-source LLM deployment frameworks like LangChain or LlamaIndex. Knowledge of reinforcement learning from human feedback (RLHF), prompt engineering, and evaluation metrics for generative models. Prior work in regulated or high-security environments (finance, healthcare, etc.) is a plus. Compensation and Benefits: Base Salary: $140,000 – $160,000 15% Annual Bonus hybrid work setup Generous benefits package including health, dental, and vision insurance Professional development budget and opportunities to attend top AI conferences How to Apply: To express your interest in this opportunity, please submit your CV via the "Apply" link on this page. We look forward to hearing from you!