The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.
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.
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
Similar Jobs
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.
Top Skills:
Ai AgentsAutomated EvaluationExperimentationFull-Stack DevelopmentGenerative AiInformation RetrievalMachine LearningProduction Monitoring
AdTech • Digital Media • Marketing Tech • Software • Automation
Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.
Top Skills:
A2AAg-UiAmazon S3AWSAws BedrockBedrock AgentcoreFastapiFastmcpLangchainLangfuseLanggraphMcpOauth 2.0PythonRagRest ApisSnowflakeVector Databases
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.
Top Skills:
Ai AgentsBigQueryClaude CodeCursorData WarehousesDatabricksDbtEtl/EltLakehousesLlmsMarketing AutomationMartechPythonSalesforceSemantic LayersSnowflakeSQLStreamlitWeb Analytics
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center



