Contributing to the deployment of Agentic AI workflows for hyper-personalized marketing campaigns across email, social, and digital advertising platforms.
Supporting the development of intelligent automation workflows for dynamic content generation, audience segmentation, and real-time bid optimization using LLM API integrations and frameworks like LangGraph.
Assisting in building and maintaining robust RAG (Retrieval-Augmented Generation) systems powered by vector databases to ensure our AI components access and leverage relevant marketing intelligence.
Learning and working with our proprietary Model Context Protocol (MCP) implementations to help ensure AI agents maintain coherent context across marketing data sources.
Writing and maintaining data pipelines to feed real-time marketing data into our AI models for continuous data collection and campaign adaptation.
First 30 Days: You'll immerse yourself in our existing marketing tech stack, data pipelines, and current AI workflows. Your immediate focus will be on understanding the core business challenges our marketing team faces and assisting senior engineers in prototyping a basic LLM API integration for a specific content generation or customer interaction use case.
First 60 Days: You will take on tasks to implement and test specific modules of an Agentic AI workflow for a key marketing channel, working with LangGraph for orchestration and connecting with our vector databases under technical guidance.
First 90 Days: You will have successfully contributed to your first end-to-end AI workflow deployment, demonstrating clear value by automating a routine task or optimizing a specific campaign process. You will monitor its initial performance and troubleshoot bugs alongside the team.
Foundational Experience: 1–2 years of practical experience (including strong academic projects or internships) working with Python, data operations, and foundational AI integrations, ideally with an interest in marketing domains.
Agentic AI Exposure: Understanding of Agentic AI architectures, multi-agent concepts, and how they apply to automated workflows.
Python Proficiency: Strong foundational proficiency in Python for writing clean, maintainable scripts, handling data manipulation, and building API integrations.
SQL Foundations: Solid SQL skills for data extraction, querying, and preparing datasets from marketing databases.
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API Integrations: Practical experience working with LLM APIs (OpenAI, Claude, Gemini) and handling standard JSON payloads for software or marketing integrations.
Machine Learning Basics: Good understanding of core machine learning principles and statistical analysis relevant to data-driven marketing.
AI Frameworks: Exposure to or structured training in AI orchestration frameworks like LangGraph, CrewAI, or AutoGen.
Vector Databases: Conceptual knowledge or basic practical experience with vector databases (Pinecone, Weaviate, Milvus, Qdrant, etc.) for semantic search.
Model Context Protocol (MCP): Eagerness to learn and adopt Model Context Protocol (MCP) concepts for keeping consistent context within agent tasks.
RAG Systems: Basic understanding of how to build and query RAG systems to enhance LLM responses with internal documentation.
Technical Communication: Ability to communicate technical progress clearly to team members and collaborate effectively with marketing stakeholders.
Experience running basic workloads on cloud platforms (AWS, Azure, GCP).
Familiarity with Git workflows, version control, and basic CI/CD concepts.
Interest in digital advertising metrics, Google Ads, or performance marketing operations.
Prior experience in a fast-paced environment where you are comfortable taking initiative and learning on the fly.
About Printerpix
Retail55 employeesFounded 2011
Family-owned e-commerce retailer creating personalized photo gifts, photobooks, blankets, canvases, calendars, and prints for consumers.