About the company Behavox is a global group of companies and a cloud-native AI company providing an integrated controls platform for global banks, asset managers, hedge funds, private equity firms, insurance businesses, and commodity firms. The platform unifies communications and trade surveillance, compliant archiving, policy management, and front-office analytics on a single, AI-native technology stack, delivered as a globally scalable SaaS-based cloud service. Our engineering culture is built around speed, experimentation, and technical excellence, following agile principles and rapid iteration. We constantly test and adopt the latest cloud technologies and AI tooling, optimizing for fast feedback loops and execution. We look for people who can move fast, challenge conventional wisdom, and want to work at the frontier of modern AI, SaaS platforms, and distributed systems. Behavox is a high-performance organization with a strong bias toward delivery, ownership, and responsibility. We are building systems that are complex, mission-critical, and global in scale. To do that, we seek highly skilled engineers and technologists who take end-to-end responsibility and want to build what others cannot. Founded in 2014 and backed by SoftBank Vision Fund, the Behavox group is headquartered in London, with offices worldwide, including New York City, Toronto, Montreal, Seattle, Singapore, and Tokyo. About the role Our Canadian entity, Northern Path Communication & Security Software Inc., is recruiting a Senior NLP Engineer for our Toronto office. The role sits within the Behavox AI team as an experienced individual contributor. It uses NLP and artificial intelligence (AI) to build features that help our clients understand and act on vast volumes of multilingual communications data (email, chat, and voice transcripts from the world’s largest financial institutions), turning them into accurate, explainable risk and compliance insights. Alongside model development, the role owns the data foundations behind it. It designs, develops, and maintains the databases and data models that power our NLP pipelines, and sets the standards for how that data is administered across the organization. Working with a high degree of autonomy, the role takes solutions from prototype to production in close partnership with research scientists, product managers, MLOps, and engineering. It also supports the revenue team with technical expertise when clients ask how our AI works. Responsibilities AI-powered communication features: Use existing NLP technologies and AI solutions, including LLMs, to develop communication assistance features and risk-detection capabilities for clients. Data science and model development: Design, train, fine-tune, and evaluate NLP models, and take them from prototype to production with MLOps and engineering. Database design and development: Design, develop, and maintain the databases behind our NLP pipelines, and develop and implement a strategic blueprint for deploying databases across the organization. Data administration and testing: Develop and implement data administration policies, standards, and models. Test data models and database management systems before and after release. Infrastructure improvement: Collect data to identify areas for improvement in the organization’s IT and data infrastructure, and turn findings into practical changes. Documentation and reporting: Document reporting needs, queries, logic, results, and recommendations for other information systems professionals. Collaboration and mentoring: Advise product and engineering teams, explain model behaviour to clients alongside the revenue team, and mentor other engineers through Git-based workflows and code review. Requirements Education: Master’s degree in computer engineering or a related field (such as computer science, computational linguistics, or machine learning). Experience: 5 or more years of experience building NLP, data science, or machine learning systems, including solutions shipped to production. Language: English. Requirements NLP and AI: Hands-on experience with transformer models and large language models (LLMs), including fine-tuning, retrieval-augmented generation (RAG), and embedding-based search, and with core tasks such as text classification, entity recognition, and relationship extraction. Data science: Experience building evaluation datasets and metrics, working with limited and imbalanced data, and explaining model behaviour to non-specialists. Databases and data modelling: Experience designing, developing, and testing databases, data models, and database management systems. Programming: Strong Python; working knowledge of C, C++, Go, Java, and JavaScript; daily use of Git. Production engineering: Experience serving, monitoring, and maintaining models and data systems in production on cloud platforms (AWS or GCP). Personal suitability: Accurate, organized, and detail-oriented, with sound judgment, initiative, and accountability. Adaptable, a quick learner, and a team player with a strong client focus and excellent written and oral communication. Work environment A fast-paced environment with tight deadlines, where you will sometimes work under pressure. Attention to detail matters because our clients operate in highly regulated industries. Benefits Salary: $170,000 to $230,000 CAD per year, depending on experience. A benefits package that includes an equity award aligned with long-term value creation, in addition to cash compensation. 30 days of paid time off annually, plus public holidays and sick leave. A hybrid role based in our modern Toronto office (1 day per week in office). The opportunity to work on a global, mission-critical AI platform alongside highly skilled engineers and technologists across multiple geographies. Real ownership and impact, building complex systems at scale in an environment that values speed, experimentation, and technical excellence. About the company We believe hiring is a two-way assessment: you are evaluating us as much as we are evaluating you. Our process looks like this: Initial conversation: We cover salary, location, core experience and skills, and values alignment. Technical interview: A deep dive into the technical competencies critical to the role. Behavioural interview: A deep dive into behavioural competencies. Practical task: The most aligned candidates complete a work simulation, typically presented and discussed with a group of colleagues and managers. Leadership meetings: You meet several of our senior leaders so you can make a fully informed decision. Please note We want a genuine conversation, so the use of AI tools or assistance during live interviews is not permitted and will result in disqualification from the process. Interviews may be recorded for internal review, to support fairness and collaborative hiring discussions within the team.
