20 hours ago
Toronto, CanadaSenior
Responsibilities
- Design, build, test, and maintain ML and AI capabilities for AIOS, focusing on knowledge retrieval, model integration, and production reliability.
- Develop embeddings, vector search, ranking, retrieval-augmented generation, knowledge ingestion, and knowledge quality systems.
- Build evaluation workflows, test sets, scoring methods, quality metrics, and monitoring for AI and LLM-based systems.
- Write production-quality Python or equivalent code for services, pipelines, automation, tests, and product integrations.
- Improve ML system performance, latency, cost, reliability, and observability across production and pre-production environments.
- Analyze model behavior and improve system quality using quantitative metrics and qualitative reviews.
- Support traceability, explainability, governance, auditability, privacy, and human review workflows for responsible AI.
- Create technical documentation, model cards, evaluation notes, implementation guidance, and operational runbooks.
- Collaborate with AI engineers, data engineers, product managers, architects, and domain experts to deliver ML system designs and software.
Requirements
- At least 5 years of software engineering, machine learning engineering, data science engineering, or applied ML experience building production systems.
- At least 2 years of hands-on experience with production ML, LLM applications, retrieval-augmented generation, NLP, search, recommendation, or AI-powered automation.
- Strong Python programming skills and experience producing maintainable, tested, production-quality code.
- Practical experience with ML frameworks, LLM APIs or open-source models, embeddings, vector databases, retrieval systems, or model-serving patterns.
- Strong understanding of data pipelines, feature or knowledge preparation, model evaluation, experiment tracking, and performance measurement.
- Solid software engineering fundamentals, including APIs, databases, testing, debugging, version control, and system design.
- Ability to explain ML trade-offs, model behavior, and technical recommendations to product and engineering stakeholders.
- Bachelor's degree in Computer Science, Engineering, Machine Learning, Statistics, Mathematics, or a related technical field, or equivalent practical experience.
- Preferred experience includes RAG systems, knowledge graphs, document understanding, semantic search, enterprise knowledge platforms, MLOps, model monitoring, model registries, cloud ML deployment, Azure, Kubernetes, Docker, distributed data processing, and regulated-industry data.
- Preferred experience also includes AI evaluation, safety testing, hallucination mitigation, prompt testing, governance controls, security, privacy, data access controls, audit trails, published research, open-source contributions, patents, or technical writing.
Benefits
- Fully company-paid health benefits, employer-paid insurance coverage, extensive paramedical benefits, and wellness resources.
- Annual bonus opportunities and RRSP matching.
- Generous vacation, holidays, paid time off, and paid volunteering days.
- Opportunities for professional growth and career advancement.
- Hybrid work arrangement.
- Accommodations are available for candidates with disabilities.
Tech Stack
Categories
About Zafin
Zafin builds enterprise platforms for regulated institutions, including banks, covering AI agent orchestration (AIOS), product and pricing, offers, billing, loyalty, and integration (Zafin IO). It sells SaaS software and related services that externalize banking logic and connect legacy and modern systems. Headquartered in Toronto, it is privately held under Nordic Capital ownership and serves financial institutions across North America, EMEA, and Asia-Pacific.