Who you are

- 5+ years of software engineering, machine learning engineering, data science engineering, or applied ML experience building production systems

- 2+ years of hands-on experience with production ML, LLM applications, retrieval-augmented generation, NLP, search, recommendation, or AI-powered automation

- Strong programming skills in Python and experience building 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 analyze model behavior, diagnose quality issues, and improve systems using both quantitative metrics and qualitative reviews

- Strong communication skills, with the 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

- Experience building RAG systems, knowledge graphs, document understanding systems, semantic search, or enterprise knowledge platforms

- Experience with MLOps practices, model monitoring, model registries, CI/CD for ML, or deployment of ML services in cloud environments

- Experience with Azure, Kubernetes, Docker, distributed data processing, or scalable data infrastructure.

Experience with banking, financial services, compliance, risk, or other regulated industry data

- Experience with AI evaluation, safety testing, hallucination mitigation,



prompt testing, or governance controls for LLM systems

- Experience collaborating with product teams to turn ambiguous AI capabilities into usable product features

- Experience with security, privacy, data access controls, audit trails, and responsible AI requirements

- Published research, open-source contributions, patents, or technical writing related to ML, NLP, search, or AI systems

What the job involves

- As a Machine Learning Engineer, part of Zafin's AIOS Product team, you will build the machine learning and AI systems that power the AIOS Knowledge Fabric and help agents reason over enterprise knowledge with accuracy, traceability, and control

- You will work on practical production systems, including retrieval, ranking, embeddings, model integration, evaluation, data pipelines, model serving, observability, and the workflows that make AI outputs reliable for regulated banking environments. This is a hands-on engineering role focused on turning ML and LLM capabilities into dependable product infrastructure

- This role is critical for high-quality knowledge, strong evaluation, and reliable model behavior. You will help build the foundation that allows customers to use AI agents with confidence, accountability, and measurable business value

- Design, build, test, and maintain ML and AI capabilities for AIOS, with a focus on knowledge retrieval, model integration, and production reliability





- Develop systems for embeddings, vector search, ranking, retrieval-augmented generation, knowledge ingestion, and knowledge quality improvement

- Build and improve model evaluation workflows, test sets, scoring approaches, quality metrics, and monitoring for AI and LLM-based systems

- Partner with AI engineers, data engineers, product managers, architects, and domain experts to translate product needs into ML system designs and working software

- Write production-quality Python or equivalent code, including services, pipelines, automation, tests, and integration points with product systems

- Improve ML system performance, latency, cost, reliability, and observability in production and pre-production environments

- Work with structured and unstructured enterprise data, including documents, metadata, knowledge sources, customer data, and operational signals

- Support responsible AI practices by improving traceability, explainability, governance, auditability, privacy, and human review workflows

- Create technical documentation, model cards or evaluation notes where useful, implementation guidance, and operational runbooks

- Stay current with ML, LLM, RAG, evaluation, and MLOps practices and bring practical improvements into the AIOS product

Benefits

- Adaptable work arrangements - includes opportunities for hybrid work, flexible hours, flexible locations, and various types of paid leave

- Meaningful recognition - we truly value our team members’ contributions and ensure that their achievements are properly recognized and rewarded through areas such as incentive opportunities

- Ownership - we offer a stock option plan that is exclusively for employees who share our sense of purpose and create value that keeps us on our A-game

📌 Senior Machine Learning Engineer (Toronto)
🏢 Zafin
📍 Toronto