Fullstack Developer - AIMarseer AI • Seattle, Washington, United States
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Fullstack Developer - AI
Marseer AI
- Seattle, Washington, United States
- Seattle, Washington, United States
À propos
You are equally comfortable designing a multi-step agentic workflow in Python and building a clean, responsive UI in React/Next.js . You understand how LLMs behave in production, know how to engineer prompts and tool chains for reliability, and care deeply about the end-to-end user experience.
Your responsibilities will span the following functions:
AI Engineering and Agent development
Design and implement multi-step AI agent workflows using LLM orchestration frameworks (e. g., LangChain, LangGraph, CrewAI, or similar).
Build and maintain RAG (Retrieval-Augmented Generation) pipelines, including chunking strategies, embedding generation, vector store management, and retrieval tuning.
Integrate with LLM providers (OpenAI, Anthropic, or others), including prompt engineering, tool/function calling, structured output generation, and context window management.
Develop AI-driven features such as campaign brief generation, audience recommendation, content variant creation, and performance insight summarization.
Design agentic systems that autonomously analyze marketing data, generate recommendations, and trigger downstream actions across channels.
Implement evaluation and observability frameworks to monitor LLM output quality, latency, and cost in production.
Full Stack development
Build and maintain frontend interfaces using React and Next.js dashboards, agent interaction UIs, campaign builders, and insight surfaces.
Design and implement RESTful and/or GraphQL APIs in Python (FastAPI or Flask) or Node.js to serve AI outputs to the frontend.
Integrate frontend with backend AI services, streaming LLM responses, and real‑time status updates.
Own the full feature lifecycle from technical design through implementation, testing, and deployment.
Ensure UI components are performant, accessible, and consistent with design system standards.
Data and integrations
Work with structured and unstructured marketing data campaign performance metrics, audience segments, content libraries, and brand strategy documents.
Integrate with third‑party marketing platforms and data sources (e. g., Klaviyo, Google Ads, Meta, Shopify) to feed the intelligence layer.
Collaborate with the damStack data engineering team to consume Snowflake‑sourced customer signals and segment outputs.
Collaboration and communication
Work directly with client brand teams to understand marketing workflows, gather feedback, and translate requirements into product features.
Communicate technical decisions and AI system behaviors clearly to non‑technical marketing stakeholders.
Participate in architecture reviews, contribute to technical design documents, and maintain clear documentation of AI systems and APIs.
Requirements
5-6 years of professional software engineering experience, with meaningful time in both backend and frontend development.
Proven experience building and deploying LLM‑powered applications in production, not just prototypes.
Strong proficiency in Python for backend/AI development.
Strong proficiency in React and Next.js for frontend development.
Hands‑on experience with LLM orchestration frameworks (LangChain, LangGraph, CrewAI, or equivalent).
Experience building RAG pipelines, vector stores (Pinecone, Weaviate, pgvector, or similar), embedding models, and retrieval strategies.
Preferred Skills
Experience designing and building multi‑agent or agentic AI systems with tool use, memory, and planning capabilities.
Familiarity with prompt engineering best practices, structured output generation, and LLM evaluation methodologies.
Experience with streaming LLM responses and real‑time UI updates (SSE, WebSockets).
Prior work in a SaaS product company shipping production features, not just internal tools.
Familiarity with marketing platforms, e‑commerce data, or martech ecosystems.
Experience with API design (REST or GraphQL) and backend service architecture.
Good to Haves
Exposure to TypeScript and modern frontend tooling (Tailwind CSS, shadcn/ui, etc. ).
Familiarity with Snowflake or other cloud data warehouses as a data source for AI pipelines.
Experience with observability tools for LLM applications (LangSmith, Helicone, Arize, or similar).Understanding of marketing concepts, segmentation, campaign lifecycle, attribution, and content personalization.
Candidate Profile
Availability to work US business hours that overlap with the US Eastern or Pacific time zones is required for client collaboration and team standups.
Strong written and verbal English communication you will regularly interact with client brand teams and must explain AI system behavior clearly to non‑technical stakeholders.
Product sense, you think about the user experience of AI features, not just whether the model output is correct.
Ownership mindset you take features end‑to‑end, flag issues early, and drive problems to resolution without being asked.
Comfort with ambiguity, LLM‑powered systems are non‑deterministic; you know how to design for reliability and graceful degradation.
Collaborative, you work well across engineering, design, and client‑facing functions in a distributed team.
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Compétences linguistiques
- English
Avis aux utilisateurs
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