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
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
Conduct AI-driven security assessments across software, hardware, and firmware; develop AI-enabled offensive and defensive security tooling; identify and remediate vulnerabilities; test LLMs, agentic workflows, and cloud environments; and guide organization-wide AI security strategy. Partner with engineering and leadership, lead threat-modeling sessions, and communicate security findings and recommendations to senior stakeholders.
Top Skills:
Adversarial TestingAgentic WorkflowsAIApplication SecurityAWSEmbedded SystemsFirmwareGitGoLlmsOffensive SecurityPrompt InjectionRagThreat ModelingVulnerability Management
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.
Top Skills:
Ai Coding AgentsCi/CdGitLlm-Based Classification ToolingMetrics StorePythonReactSnowflakeSQLTypescript
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.
Top Skills:
Ai Coding AgentsCi/Cd PipelinesDbt Semantic LayerGitGoverned Metrics StoreLlm-Based Classification ToolingLookmlPythonReactSnowflakeSQLTypescript
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory



