MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Senior Machine Learning Engineer designing and optimizing machine learning systems for insurance and investment solutions at Equisoft. Focusing on large language model fine-tuning and synthetic data generation initiatives.
Responsibilities
- Lead fine-tuning initiatives for open-source LLMs (such as Llama, Mistral, and domain-specific models) to optimize performance for insurance and financial services use cases
- Design and implement scalable synthetic data generation pipelines using advanced techniques including GANs, VAEs, and LLM-based generation methods
- Create and curate insurance-specific datasets for model training, ensuring compliance with privacy regulations and industry standards
- Optimize machine learning models for cost-effective inference, implementing techniques such as model distillation, quantization, and efficient deployment strategies
- Collaborate with the cloud developer (our ML architecture team) on overall system design, model integration, and performance optimization
- Establish and maintain best practices for model versioning, deployment, and monitoring using MLOps frameworks
- Develop and maintain automated model evaluation pipelines to ensure consistent performance and quality
- Research and implement state-of-the-art ML techniques including transfer learning, few-shot learning, and domain adaptation
- Monitor model performance in production and implement continuous improvement strategies
- Work cross-functionally with product and engineering teams to integrate ML models into customer-facing applications
Requirements
- Technical Bachelor's Degree in Computer Science, Machine Learning, Data Science, or Engineering, or College Diploma combined with 5+ years of relevant experience
- Minimum of 4 years' experience in machine learning engineering, with demonstrated expertise in model development, deployment, and optimization
- Extensive experience with Python and ML frameworks including PyTorch, TensorFlow, Hugging Face Transformers, and scikit-learn
- Proven experience in fine-tuning large language models (LLMs) for domain-specific applications
- Strong background in synthetic data generation techniques and data augmentation methods
- Experience with cloud platforms (AWS, Azure, or Google Cloud) and their ML services
- Proficiency in MLOps tools and practices including model versioning, deployment pipelines, and monitoring
- Knowledge of distributed computing frameworks (Apache Spark, Dask) for large-scale data processing
- Experience with containerization technologies (Docker, Kubernetes) for model deployment
- Understanding of statistical modeling, deep learning architectures, and optimization techniques
- Excellent knowledge of French & English (spoken and written)
Benefits
- Medical
- Dental
- Retirement Plan
- Telemedicine Program
- Employee Assistance Program
- Flexible hours
- Educational Support (LinkedIn Learning, LOMA Courses and Equisoft University)
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