Seniority: Senior · Location: Remote · Work model: 100% remote / work from home · Employment type: Full-time · Time zone: Flexible schedule based on project needs, aligned with core leadership hours
About the company
Our client is a specialized digital marketing agency that helps brands build real trust and meaningful engagement on Reddit. The company combines organic community strategy with the growing influence of Reddit content in search and AI-generated answers. It operates as a lean, international-minded team where individual contributions have direct impact. The business also builds advanced internal automation and AI systems to support its operations.
About the role
This role exists to organize, improve, and consolidate a substantial internal ecosystem of AI tools, automation workflows, and multi-step agents. Working directly alongside the founding leadership, you will evaluate technical requirements, select practical architectures, and own the delivery of end-to-end solutions. Your work will make AI and automation systems more reliable, maintainable, cost-effective, and usable across the team.
What you'll do
- Build, test, troubleshoot, and maintain scalable workflows and automations using n8n.
- Integrate REST and GraphQL APIs, webhooks, relational and non-relational databases, and diverse third-party platforms.
- Evaluate technical trade-offs and choose the simplest, most effective architecture, balancing low-code automation with custom script development.
- Write clean, efficient code in JavaScript/TypeScript or Python when custom logic or data processing is required.
- Implement LLM capabilities and AI model integrations directly into core business operations and internal tools.
- Use agentic coding tools such as Claude Code or Codex every day; configure them with rules, skills, and project context; and review, debug, and test generated code for accuracy and security.
- Translate broad business targets into incremental technical releases and validate core assumptions early in the development cycle.
- Conduct rigorous quality assurance against edge cases, common failure modes, data integrity risks, and usability bottlenecks.
- Manage credentials, sensitive data, and environment configurations in accordance with security best practices.
- Produce clear, concise system documentation so solutions remain maintainable and operational after initial deployment.
- Structure documents, conversations, and other source material into consistent, usable data while preserving source references and access boundaries.
- Manage context by deciding which instructions, information, tools, and previous work each model needs at each step.
- Preserve useful context across longer projects and reduce unnecessary processing through effective context engineering.
- Choose which model handles each task, define the order of steps and handoffs, and use lower-cost models where testing shows they meet the quality standard.
- Track accuracy, errors, token usage, subscription limits, cost, and completion time to identify waste and unreliable results.
- Build repeatable tests for real business tasks and compare new models with the current setup before introducing improvements.
- Stabilize and connect workflows using reusable components and consistent build methods.
- Provide clear instructions and demonstrations, and improve tools based on team feedback.
- Establish one source of truth for every project, define shared build standards, and add audit steps for workflows before they reach production, with GitHub as the source of truth.
- Take full ownership of the AI systems built, improve how they work together, and make them usable for the internal team.
Must-have
- Senior-level experience with the ability to explain context engineering and model-selection methods in your own words, using examples from systems you have built.
- Demonstrated, hands-on experience building, debugging, and deploying real-world automation workflows in n8n.
- Proven context engineering experience on large multi-step or multi-agent projects, including the ability to explain clearly how it differs from prompt engineering.
- Hands-on experience comparing multiple models, including Claude, GPT, Gemini, and open-source models such as Kimi, GLM, or Qwen.
- A clear method for choosing models based on quality, cost, and speed.
- Working knowledge of model settings such as temperature and top-p.
- Practical development skills in JavaScript/TypeScript and/or Python for custom backend logic and data parsing.
- A proven track record working with webhooks, RESTful APIs, database connections, and third-party software ecosystems.
- Hands-on experience embedding LLM endpoints, AI capabilities, and prompt architectures into practical workflows.
- Ability to audit and validate AI-generated code critically rather than relying on automated outputs without verification.
- Strong research skills and an engineering mindset focused on practical, maintainable solutions rather than over-engineering.
- High-level written and verbal English skills for effective collaboration with founding leadership and technical teams.
- A dedicated quiet workspace equipped with a high-performance computer, reliable backup systems, and high-speed internet.
Nice-to-have
- A portfolio demonstrating completed AI workflows, custom script builds, or automated operations tools.
What's offered
- High impact and autonomy, with direct ownership of end-to-end technical solutions and minimal administrative friction.
- Direct collaboration with founding leadership to shape technical strategy and operational roadmaps.
- The opportunity to work on advanced AI, LLM, automation, and internal tooling initiatives.
- A 100% remote work model that enables you to execute advanced AI and automation workflows from anywhere.
- Schedule flexibility based on project needs, aligned with core leadership hours.
Hiring process
- Intro call with our recruitment team
- Interview with the hiring team
- Discussion of your context engineering and model-selection experience, including examples of systems you have built
- Offer