• Design, build, and maintain production-grade AI systems and customer-facing AI features • Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services • Build backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows • Design and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly • Integrate foundation models through platforms such as Amazon Bedrock or Agent Core • Develop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases • Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes • Monitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency • Debug AI behavior through logs, traces, evaluations, user feedback, and production telemetry • Collaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems • Help establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability • Mentor and collaborate with engineers across both software and AI disciplines • Evaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability

• Help scale modular tools, intelligent workflows, and production-grade RAG systems • Mature existing AI proofs-of-concept into reliable production systems while establishing engineering rigor through TDD, CI/CD automation, and operational monitoring • Partner closely with AI Champions across departments to create reusable automation capabilities

Role Description At Portless, we specialize in global delivery solutions for SMBs and enterprise merchants, enabling businesses to ship direct-from-factory from manufacturing hubs like China to destinations worldwide. As an AI Engineer, you will own the design, development, and deployment of AI-powered systems that make our operations faster, our team smarter, and our merchants more successful — from intelligent automation and agentic workflows to LLM integrations embedded across our product and internal tooling. If you're passionate about building AI systems that create real business impact, thrive in fast-moving environments, and want to work at the intersection of logistics and cutting-edge AI, we'd love to meet you. - Design and build AI-powered features across our B2B portal, internal tooling, and merchant-facing products — including LLM integrations, AI agents, and intelligent automations - Translate ambiguous business problems into well-scoped AI solutions, from prompt engineering and RAG pipelines to full agentic workflows - Build, evaluate, and iterate on AI systems using a rigorous experiment-driven approach — tracking quality, latency, and cost tradeoffs - Collaborate closely with product, operations, and engineering teams to identify high-leverage AI opportunities and deliver them end-to-end - Develop internal AI tooling and skill frameworks that empower non-technical teams to leverage AI in their daily workflows - Integrate with third-party AI APIs (Anthropic, OpenAI, etc.) and MCP-based tooling while maintaining security and reliability standards - Maintain observability over deployed AI systems — monitoring for regressions, prompt drift, and model performance degradation - Work independently in a remote environment with a strong sense of ownership and ability to ship with minimal oversight Qualifications - 3+ years of software engineering experience, with at least 1–2 years focused on building production AI or ML systems - Hands-on experience with LLM APIs (Anthropic Claude, OpenAI GPT, etc.) and prompt engineering best practices - Strong programming skills in Python and/or TypeScript/JavaScript; comfortable building both backend services and lightweight frontend interfaces - Experience building RAG pipelines, embedding workflows, or agentic systems using frameworks like LangChain, LlamaIndex, or similar - Familiarity with vector databases (Pinecone, Weaviate, pgvector, etc.) and semantic search patterns - Experience working cross-functionally with non-technical stakeholders to scope and deliver AI projects - Proven ability to evaluate AI output quality and build evals/testing frameworks for LLM-based systems - Logistics, supply chain, or B2B SaaS experience is a strong plus - Experience with MCP (Model Context Protocol), AI agent orchestration, or multi-step tool-use workflows is a bonus

• Serve as the primary point of contact for department champions across Omada, working with them to understand their day-to-day workflows and identify where AI-assisted automation creates real leverage. • Drive AI adoption across the company by deploying tool bundles that are actually useful, making sure integrations fit how teams work rather than asking teams to adapt to what's technically convenient. • Run a continuous feedback loop: gather usage signals and qualitative input from champions, identify what's working and what's falling flat, and iterate accordingly. • Build lightweight documentation, reference examples, and enablement materials that help non-technical stakeholders understand what's possible and how to request new capabilities. • Represent the needs of end users and champions when making integration decisions; you are their voice in the build process. • Integrate SaaS and internal applications with Omada's MCP ecosystem by building and configuring MCP servers, adding tools, and handling authentication patterns including OAuth and webhooks. • Write skills that expose the right actions and data to AI agents in a clear, composable way. • Compose skills and tools into role-appropriate bundles, scoped to what each team and function actually needs, not everything at once. • Deploy tool bundles to ABAC groups, managing access so the right people get the right capabilities without overprovisioning. • Partner with the Senior IT Engineers, Automation on integrations that span MCP and workflow automation, ensuring handoffs and shared patterns are consistent. • Participate in design reviews for new MCP integrations to catch potential issues early and keep the ecosystem coherent. • Teach and mentor IT team members as you go. Be the SMEs that help us understand and internalize this tech.