Senior Machine Learning Engineer

What is Equisoft? Equisoft is a global provider of digital solutions for insurance and investment, recognized by over 250 of the world's leading financial institutions. We offer a comprehensive ecosystem of scalable solutions that help our customers meet all the challenges brought about by this era of digital transformation, thanks to our business needs-driven approach, industry knowledge, cutting‑edge technologies and experts. With its business-driven approach, in-depth industry knowledge, cutting‑edge technologies and multicultural team of experts based in North America, the Caribbean, Latin America, Europe, Africa, Asia and Australia, Equisoft helps its customers meet the challenges of this era of digital transformation. Why Choose Equisoft? With 950+ Employees, we are a stable organization that offers career advancement opportunities and fosters an environment of stimulation. Hiring Location: Canada (Montreal or Quebec City) You are working hybrid in a collaborative workspace Internal job title: Senior Machine Learning Developer Full-time Permanent Benefits available day 1: Medical, Dental, Retirement Plan, Telemedicine Program, Employee Assistance Program, etc. Flexible hours Number of hours per week: 40 Educational Support (LinkedIn Learning, LOMA Courses and Equisoft University) Role The Senior Machine Learning Engineer reports to the AVP, Core Insurance and works closely with AI/ML teams, product managers, and software engineering teams. The incumbent will be responsible for designing, implementing, and optimizing machine learning systems that power Equisoft's insurance and investment solutions, with a particular focus on large language model fine‑tuning and synthetic data generation initiatives. Equisoft/amplify Our AI-powered insurance ecosystem leverages advanced integration technologies to connect intelligent agents with core business systems. The platform utilizes Model Context Protocol (MCP) servers and agentic workflow orchestration to enable seamless data exchange, automated decision-making, and intelligent process automation across policy management, claims processing, and customer service applications. Your Day With Equisoft 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) Soft Skills Strong analytical and problem‑solving abilities with attention to detail Excellent communication skills for presenting complex technical concepts to diverse stakeholders Ability to work collaboratively in cross‑functional teams and manage multiple projects simultaneously Strong sense of organization and prioritizing in fast‑paced environments Adaptability to rapidly evolving ML technologies and industry best practices Team spirit, autonomy, and discipline in delivering high‑quality solutions Nice To Haves Familiarity with transformer architectures and attention mechanisms Knowledge of differential privacy and federated learning techniques Experience with A/B testing and experimental design for ML systems Contributions to open‑source ML projects or published research Certifications in cloud ML platforms (AWS ML Specialty, Google ML Engineer, Azure AI Engineer) Experience with real‑time inference systems and low‑latency model serving Knowledge of regulatory compliance in financial services (GDPR, SOX, etc.) Experience with insurance or financial services domain knowledge Equisoft is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. #J-18808-Ljbffr