Sr. Full-Stack AI Engineer

Senior Full-Stack AI Engineer We are building enterprise intelligence and orchestration technology that connects data, AI, business logic, and user experiences across industries including hospitality, travel, commerce, construction, energy, and enterprise operations. We are looking for a highly capable Senior Full-Stack AI Engineer who can independently take products and client solutions from concept through production. This is not a role for someone who only knows how to call an LLM API. We need an engineer who understands modern application architecture, enterprise integrations, data, AI systems, agentic workflows, security, and production deployment. What You’ll Do Build full-stack AI-powered applications from concept through deployment Develop modern web interfaces, dashboards, portals, and enterprise applications Design and build backend services, APIs, databases, and integration layers Integrate multiple AI models and providers using platforms such as OpenRouter Build model-routing, fallback, tool-calling, and structured-output workflows Work with OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek and other model ecosystems as appropriate Develop AI agents and multi-step workflows that interact with enterprise systems and APIs Implement RAG, semantic search, embeddings, vector retrieval, memory, and contextual intelligence Connect fragmented enterprise data sources, CRMs, APIs, databases, SaaS platforms, CMS environments, and third-party systems Build secure authentication, authorization, permissions, and role-based access Work within existing client technology environments rather than assuming everything should be rebuilt Evaluate when an LLM is appropriate and when deterministic logic, traditional ML, rules, search, or other approaches are better Optimize AI applications for latency, cost, reliability, accuracy, and scalability Create testing, monitoring, logging, error handling, and fallback mechanisms Collaborate directly with Solution Architects, product leadership, data specialists, and client technical teams Participate in technical discovery and translate architecture into working software Document systems so another engineer can understand, maintain, and extend them Technical Experience Strong experience with several of the following is expected: Frontend React Next.js TypeScript / JavaScript Modern responsive application development Backend Python Node.js / TypeScript REST and/or GraphQL APIs Event-driven and asynchronous architectures Microservices or modular service architectures AI / LLM OpenRouter OpenAI APIs Anthropic / Claude Gemini Multi-model architectures Function/tool calling Structured outputs Agentic workflows Prompt and context engineering RAG Embeddings Vector databases Model evaluation Guardrails LLM observability Token and inference-cost optimization Experience with frameworks such as LangGraph, LangChain, LlamaIndex, Vercel AI SDK, LiteLLM or equivalent tools is valuable, but we care more about architecture judgment than allegiance to a specific framework. Data PostgreSQL SQL Redis pgvector and/or vector databases Data modeling Data ingestion Entity resolution Data normalization and deduplication Connecting disparate enterprise data sources Cloud / Infrastructure Azure, AWS and/or GCP Docker CI/CD Serverless and containerized deployments Secrets management Logging and monitoring Production debugging Enterprise Integration Experience Candidates should be comfortable working with systems such as: WordPress and headless CMS platforms HubSpot, Salesforce or other CRM environments Enterprise databases Search platforms Payment systems Hospitality/travel APIs Internal enterprise APIs Identity providers Third-party SaaS platforms You should be able to look at an existing system, understand what should remain, identify what needs improvement, and integrate new capabilities without unnecessarily rebuilding the entire platform. What We Care About Beyond Code Ownership If you own a deliverable, you own it from beginning to end. We expect engineers to identify blockers, communicate risks early, propose solutions, and move work forward without needing constant follow-up. Communication If you are going to miss a meeting, deadline, or commitment, communicate it before it becomes a problem. Our engineers work with executives, enterprise clients, architects, product teams and other developers. Clear and proactive communication is a requirement of the position. Reliability We are building enterprise products. Showing up when expected, meeting commitments, documenting work, and communicating changes are part of engineering performance. Judgment We do not want technology for technology’s sake. A strong candidate can answer: Should this use an LLM at all? Which model is appropriate and why? What should remain deterministic? What data should never leave the client’s environment? What needs to be real-time? What can be asynchronous? What should we build versus integrate? What belongs in phase one versus later? What Success Looks Like You can receive a business problem such as: “Our client has thousands of customer records spread across several systems and wants an intelligent advisor platform that identifies opportunities, provides recommendations, and securely orchestrates actions.” …and independently help turn that into: Requirements → Architecture → Data → APIs → AI → Interface → Testing → Production without needing someone to define every individual task for you. Ideal Candidate You are likely a strong fit if you: Have 5+ years of professional software engineering experience Have significant recent experience building production AI applications Have shipped products, not only prototypes Are equally comfortable discussing APIs, databases, frontend experiences, AI models, and system architecture Understand enterprise security and data sensitivity Can collaborate with architects while still independently owning implementation Are comfortable in a fast-moving startup and client-delivery environment Take initiative Communicate early Solve problems instead of waiting for instructions Care about the quality of what happens after the demo Engagement: Contract, with potential to expand based on performance and company needs. Candidates should be prepared to discuss and demonstrate prior production work. A technical exercise may be included in the selection process.