Agent Engineer

Zafin•Toronto, ON

2d•CA$80,000 - CA$190,000•Hybrid

About The Position

Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments. Our portfolio includes Zafin AIOS, an agent orchestration platform for governed AI work; the Zafin Banking Platform, which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO, an integration platform that connects data, systems, and workflows across complex enterprise environments. Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale. The Agent Engineer designs, builds, integrates, tests, deploys, and continuously improves and scales the AI agents, workflows, prompts, tools, and supporting software capabilities that power Zafin's AI Operating System (AIOS) within client banking environments. This role translates approved business intent and architecture into reliable, production-grade AI capabilities that address real banking or regulatory client requirements. Working within a cross-functional AIOS squad, this role partners with Agent Architects, Industry Consultants, Evaluation Engineers, and many other professionals to ensure AI agents reflect intended business logic, integrate effectively within enterprise systems, operate safely, and meet agreed quality and performance standard. The role follows the AIOS delivery lifecycle — from Analysis & Concept, Immediate Development, Reliability Testing, and Assisted Deployment — compressing delivery timelines while holding to the platform's core principle: reliability first, velocity second. This standard reflects Zafin's own AI engineering practice. Agents, prompts, and integrations built in this role are designed to become reusable assets within the shared AI platform library used across client engagements. Scope, independence, and technical leadership increase with experience. Early-career engineers deliver defined components with guidance. More experienced engineers own complex capabilities and workstreams, resolve ambiguous implementation challenges, improve engineering practices, and guide other engineers. At the most senior level covered by this profile, the Lead Agent Engineer serves as the squad’s technical implementation anchor, leading delivery from architecture through production while partnering with the Agent Architect on solution direction.

Requirements

Typically, 1–10+ years of experience in software or AI engineering, automation, data engineering, or related technical roles, with levels determined by demonstrated scope, technical depth, independence, and leadership capability.
Degree in Comp Sci, Comp Eng, Engineering, or other technical field, or equivalent or practical experience.
Proficiency in at least one relevant programming language, such as Python, TypeScript, or Java.
Understanding of software engineering fundamentals, including source control, testing, debugging, code review, APIs, data structures, and deployment practices.
Understanding of large language model fundamentals, prompt design, tool use, and common approaches for building AI-powered applications.
Experience or demonstrated ability building software applications, automation workflows, data-enabled systems, or AI solutions.
Familiarity with integrating APIs, enterprise applications, databases, and structured and unstructured data sources.
Understanding of testing and evaluation practices for AI-driven systems, including behavioural, reliability, and safety evaluation.
Familiarity with cloud-native applications, CI/CD, monitoring, logging, observability, and production-support practices.
Ability to interpret technical designs and translate them into maintainable working implementations.
Understanding of privacy, security, data governance, and responsible AI practices applicable to enterprise AI systems, particularly with regulated industries.
Strong analytical and debugging skills.
Familiarity with AI engineering and delivery metrics (pull request acceptance rate, autonomous merge rate, cost per accepted change) as core measures of engineering quality.

Nice To Haves

Domain knowledge of banking, financial services, or large-scale enterprise IT environments.
Experience building internal AI tools or platforms adopted by other engineering teams.

Responsibilities

Build, configure, and optimize AI agents, tools, workflows, prompts, integrations, and supporting application components.
Build and maintain AI-powered conversational experiences, intelligent search capabilities, and automation solutions alongside core agent workflows.
Translate business rules, workflow designs, and technical specifications, into production-grade AI agent capabilities.
Develop AI-powered conversational experiences, intelligent search capabilities, automation solutions, and multi-step agent workflows.
Integrate APIs, enterprise systems, tools, databases, and structured and unstructured data sources into AIOS solutions.
Write clean, maintainable, secure, and testable code and configuration that supports ongoing operation and reuse.
Design, develop, and optimize data pipelines that support AI applications and enable effective use of structured and unstructured enterprise data.
Configure and troubleshoot AI agents, workflows, templates, integrations, and supporting services to maintain production reliability.
Execute the full AIOS delivery lifecycle on assigned initiatives — Analysis & Concept, Immediate Development, Reliability Testing, and Assisted Deployment — with reliability as the primary standard.
Implement appropriate monitoring, logging, error handling, observability, security controls, and operational support practices.
Respond to evaluation findings, production feedback, and changing business requirements by refining agent behaviour, prompts, workflows, integrations, and supporting code.
Engage in rapid experimentation and continuous post-launch iteration to ensure AI agent solutions meet their targets and sustain adoption.
Contribute reusable AI agent components and design patterns to the shared platform library used across client engagements.
Partner and collaborate cross-functionally in squad to achieve client / engagement outcomes like Industry Consultants, Knowledge and Governance leaders to encode business and regulatory logic into AI agent behavior.
Configure and troubleshoot AI agents, workflows, and templates to maintain production reliability.
Participate in customer workshops, technical discussions, solution demonstrations, and knowledge transfer sessions.
Participate in the weekly AI agent design and delivery cadence, and the bi-weekly evaluation and AI agent optimization cycle.
Contribute to the platform's AI agent and DORA metrics library to ensure consistent measurement across initiatives.
Independently own increasingly complex AI agent capabilities or technical workstreams from design through production, solving implementation, integration, reliability, and performance challenges.
Lead major implementation initiatives, coordinate technical dependencies across the squad, and contribute to architecture and design decisions by identifying technical trade-offs, risks, and opportunities for improvement.
Provide technical guidance, mentorship, and code review to other Agent Engineers while strengthening engineering practices related to testing, CI/CD, observability, reliability, deployment, and code quality.
Serve as the technical implementation lead for complex AIOS initiatives, translating architecture into production-ready solutions, driving engineering excellence, and ensuring high-quality, reliable delivery across the squad.