Software Architect building next - generation AI agents and automation for Jerry.ai’s insurance marketplace. Scaling voicebots, LLM systems, and external - system workflows across physical - asset services.
About the role
- AI Engineer at Portless developing AI-powered systems for global delivery solutions. Collaborating with teams to design and implement intelligent automation and workflow systems.
Responsibilities
- 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
Requirements
- 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
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