Sr. Machine Learning Engineer
| Company | Enable |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- 5+ years of experience in machine learning engineering, applied AI, or related fields.
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related technical discipline.
- Strong foundation in machine learning and data science fundamentals—including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering.
- Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments.
- Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow.
- Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex).
- Hands-on experience with fine-tuning and distillation of large language models.
- Comfortable with cloud platforms (Azure preferred), CI/CD tools, and containerization (Docker, Kubernetes).
- Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar.
- Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders.
Responsibilities
- Design, build, and deploy RAG systems, including multi-agent and AI agent-based architectures for production use cases.
- Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation.
- Build evaluation pipelines to benchmark LLM performance and continuously monitor production accuracy and relevance.
- Work across the ML stack—from data preparation and model training to serving and observability—either independently or in collaboration with other specialists.
- Optimize model pipelines for latency, scalability, and cost-efficiency, and support real-time and batch inference needs.
- Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration.
- Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents, and evaluate their practical applicability.
- Participate in roadmap planning, design reviews, and documentation to ensure robust and maintainable systems.
Preferred Qualifications
- PhD in Computer Science, Machine Learning, Engineering, or a related technical discipline.
- Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval.
- Familiarity with prompt engineering and building evaluation pipelines for generative models.
- Exposure to Snowflake or similar cloud data platforms.
- Broader data science experience, including forecasting, recommendation systems, or optimization models.
- Experience with streaming data pipelines, real-time inference, and distributed ML infrastructure.
- Contributions to open-source ML projects or research in applied AI/LLMs.
- Certifications in Azure, AWS, or GCP related to ML or data engineering.