Forward-Deployed Engineer

About RianRian is an early-stage technology company that partners with businesses to solve operational problems through custom software and applied AI systems. We work across industries, with a focus on organizations that are underserved by existing technology solutions. Our team designs, builds, and deploys production systems that solve real operational challenges for our clients, working directly alongside them from problem discovery to deployed solution. Why We're HiringAs our client engagement volume grows, we need additional engineering capacity that can operate close to the client, not in isolation from them. This role is for someone who wants to own systems end to end: understand a messy real-world workflow, design an architecture for it, build it, and keep it running in production. What You'll DoOwn technical delivery on client engagements from requirements through deployment, monitoring, and iteration.Design and implement backend services and data pipelines that integrate with client systems of record (spreadsheets, ERPs, internal tools, third-party APIs) where clean APIs often don't exist.Build applied AI features where warranted: RAG pipelines, document/OCR extraction, agentic workflows, or predictive/recommendation systems, choosing the right tool rather than defaulting to LLMs for everything.Implement automation for workflows currently run manually or via disconnected tools (RPA, scheduled jobs, browser/desktop automation, structured data extraction).Design for auditability and correctness in systems handling operational data: versioning, field-level audit logs, role-based access, and validation against source-of-truth data.Write deployment-ready code: tests, error handling, logging, and documentation sufficient for a client team (often non-technical) to trust and maintain the system.Participate in technical scoping directly with clients, translating ambiguous operational pain points into a concrete architecture and implementation plan. What We're Looking For2+ years of professional experience building and shipping backend or full-stack systems in production, not just personal or academic projects.Strong fundamentals in at least one modern backend stack (e.g., Python/FastAPI or Django, Node/TypeScript, or similar) and comfort designing data models and APIs from scratch.Practical experience with at least one of the following: LLM application development (RAG, agentic systems, structured extraction), workflow/RPA automation, ETL or data integration pipelines, or applied ML for document or data processing.Working knowledge of relational databases and at least conceptual familiarity with graph-based or knowledge-graph data models.Experience integrating with third-party or legacy systems that lack clean APIs (Google Sheets, internal tools, exports/imports, scraping where appropriate).Comfortable owning a project with minimal specification and significant ambiguity, and communicating trade-offs to non-technical stakeholders.Cloud deployment experience (AWS, GCP, or similar) and basic familiarity with CI/CD practices. Nice to HaveExperience with Neo4j or other graph databases, entity extraction, or ontology design.Experience with browser/desktop automation frameworks (e.g., Playwright, Selenium, UiAutomation-style tools).Experience with OCR or document intelligence pipelines (layout parsing, structured extraction from PDFs/images).Bilingual capability, particularly English/Korean, for cross-border client or team coordination.Prior experience in a consulting, agency, or embedded engineering role with direct client exposure. StructureThis role begins as contract or part-time work tied to specific projects, with the possibility of expanding into a full-time position as engagement volume grows. Compensation is $60–$80 per hour depending on experience, with equity on the table for the right long-term fit.