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AI Engineer - Full Stack Developer

💼 Full-time🗓 2026-06-25

Core

Design and implement multi-step AI agent workflows, RAG pipelines, and LLM integrations on the backend while building responsive frontend interfaces for marketing teams and brand operators.

Role type

Senior AI Engineer - Full Stack Developer

Builds

AI agent pipelines, LLM integrations, RAG-based intelligence systems, and frontend dashboards for campaign management and insights.

Domain

Marketing Technology / Generative AI

Deliverable

production ML models | product features

Required skills

Python, React, Next.js, LLM orchestration (LangChain, LangGraph, CrewAI), RAG pipeline design, prompt engineering, RESTful/GraphQL API design, vector store management, observability frameworks.

Preferred skills

Experience with marketing platforms (Klaviyo, Google Ads, Meta, Shopify), Snowflake data integration, agentic system design.

Technologies

Python, React, Next.js, FastAPI, Flask, Node.js, LangChain, LangGraph, CrewAI, OpenAI, Anthropic, Snowflake, Klaviyo, Google Ads, Meta, Shopify.

Responsibilities

Design multi-step AI agent workflows; Build and maintain RAG pipelines; Integrate with LLM providers; Develop AI-driven features for campaign briefs and recommendations; Build frontend interfaces using React and Next.js; Design and implement APIs to serve AI outputs.

Seniority

Senior, hands-on IC

Rewrite
## About the Role We are looking for an experienced AI Engineer - Full Stack Developer to join the Marseer platform team. This is a dual-track role: you will build and maintain AI agent pipelines, LLM integrations, and RAG-based intelligence systems on the backend, while also owning the frontend interfaces that surface insights, recommendations, and campaign outputs to marketing teams and brand operators. You should be equally comfortable designing multi-step agentic workflows in Python and building 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. ## Responsibilities ### AI Engineering & Agent Development - Design and implement multi-step AI agent workflows using LLM orchestration frameworks such as LangChain, LangGraph, CrewAI or similar. - Build and maintain RAG pipelines, including chunking strategies, embedding generation, vector store management and retrieval tuning. - Integrate with LLM providers such as 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, including 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 experiences 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 & Integrations - Work with structured and unstructured marketing data, including campaign performance metrics, audience segments, content libraries and brand strategy documents. - Integrate with third-party marketing platforms and data sources such as Klaviyo, Google Ads, Meta and Shopify. - Collaborate with data engineering teams to consume Snowflake-sourced customer signals and segment outputs. ## Requirements ### Non-Negotiable - 5-10 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
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