Toronto or Kitchener/Waterloo, Canada (Hybrid) DataBraid is building a modern AI platform for insurance connectivity. We help brokers and carriers work across fragmented systems through unified APIs, governed browser agents, knowledge systems, and AI‑assisted workflows. Insurance depends on many systems that do not yet connect cleanly. Where carrier APIs exist, DataBraid can integrate directly. Where they do not, our agents interact with carrier portals the way a broker would, under broker control, with auditability, monitoring, and enterprise‑grade safeguards. We are AI‑first in both product and operations. Generative AI, agentic AI, agent‑assisted development, reusable knowledge bases, and human‑in‑the‑loop workflows are part of how we build, not just features we ship. This is an early engineering role at the foundation‑building stage of the company. You will work directly with the CTO and founding team to help design the system, build the product, and shape how an AI‑first startup operates. Why Join DataBraid? Work on applied agentic AI - build agents that interact with real systems, support complex workflows, and operate with human oversight. Join early - help shape the product, architecture, engineering culture, and technical standards while the foundation is still being formed. Create and explore - work in an environment where thoughtful experimentation, rapid prototyping, and practical innovation are expected. Own meaningful work - take product capabilities from problem definition through design, implementation, release, and production learning. Work with venture backing - DataBraid is backed by Koru, a venture studio funded by the Ontario Teachers' Pension Plan. What You'll Do Own major product capabilities from problem definition through production release. Build the execution layer that connects unified APIs, browser agents, workflow orchestration, structured knowledge, and broker approval. Design secure, auditable automation flows for authentication, permissions, retries, rate limits, monitoring, and human oversight. Use AI agents and generative tools to explore implementation options, improve specifications, generate tests, and raise engineering quality. Convert recurring product, domain, and engineering knowledge into reusable specs, evaluations, examples, and context for people and agents. Work directly with the CTO, founding team, and early customers to make product and architecture decisions. What You Bring We are looking for a senior full‑stack engineer with hands‑on agentic AI experience: a systems‑minded builder who can define the problem, shape the approach, and ship reliable software. 7+ years of professional software engineering experience across frontend, backend, APIs, data, and cloud infrastructure. Ability to ship production‑quality software across a Python and TypeScript stack; our current environment includes Next.js, Django, PostgreSQL, Docker, Kubernetes, and AWS or similar cloud infrastructure. Strong engineering fundamentals across distributed systems, API design, data modeling, testing, observability, security, and production operations. Hands‑on experience with generative and agentic AI, such as tool‑using LLM systems, function calling, workflow orchestration, RAG, evaluation loops, or human‑in‑the‑loop approval flows. Ability to use AI‑assisted development effectively while maintaining high standards for architecture, code review, testing, and production reliability. Early‑stage product mindset: ability to create structure, make thoughtful tradeoffs, iterate quickly, and see work through from concept to customer impact. How You'll Stand Out Prior insurance experience is helpful but not required. The strongest fit is someone familiar with the underlying class of problems: complex workflows, third‑party systems, regulated data, and automation that must be reliable, observable, and trusted. Relevant experience may include: Browser automation, workflow engines, RPA, integration infrastructure, or systems that interact with complex third‑party portals. Agentic systems that operate against real tools, external interfaces, or business workflows where planning, evaluation, fallback, and human approval matter. Systems with audit trails, permissions, rate limits, retries, fallbacks, and operational visibility. Knowledge systems involving structured and unstructured data, semantic search, domain vocabulary, or reusable context. Translating complex business processes into clear specs, system boundaries, tests, and release expectations. Insurance, fintech, B2B SaaS, developer platforms, or other integration‑heavy environments. Process and Package Our process is practical and direct: an intro call with the CTO, technical problem‑solving, product and engineering conversations, system design, and a final discussion with DataBraid's CEO. We offer competitive salary, early‑hire equity, hybrid flexibility with regular Toronto working sessions, direct access to the founding team, and the opportunity to help define an AI‑first engineering culture from the beginning. #J-18808-Ljbffr