Fullstack Developer - AI

We are looking for an experienced AI Engineer or 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 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.

Skills

CrewAI, LLM, LangChain, LangGraph, Next.js, Python, RAG, React