Senior Artificial Intelligence Architect

About

Mod Op is redefining how marketing gets done. We’re building AI-enabled solutions that transform creativity, strategy, and execution. We are reimagining how the agency‑client and business‑consumer/client value equation looks, feels and delivers unforgettable experiences and services at scale.

As an AI Engineer on Mod Op’s Innovation Team, you’ll help build the future of marketing by combining data, behavioral insights, and cutting‑edge AI technology to create intelligent tools and agents that elevate creativity, drive efficiency, and unlock growth for global brands.

You’ll work alongside strategists, data scientists, creatives, and technologists to design and deploy bespoke AI solutions that scale across industries like CPG, entertainment, food & beverage, and B2B. Your curiosity about human behavior and your drive to experiment will power innovations that reshape how customers discover, engage with, and buy products and services.

This is not a lab role. It is a build, test, learn, and ship role. You’ll help Mod Op and our clients evolve faster, smarter, and more humanly.

Responsibilities

• Design comprehensive AI system architectures, taking responsibility for selecting models (e.g., LLMs, traditional ML, or hybrid approaches), determining the use of RAG versus fine‑tuning or agentic workflows, and overseeing data pipelines, vector stores, orchestration, and observability.
• Proactively assess scale, latency, cost, and reliability tradeoffs, collaborating with IT to devise solutions and monitoring tools to prevent issues before they arise.
• Partner with product, strategy, and executive stakeholders to define the problems at hand, transform ambiguous goals into specific solutions and requirements, and establish and promote shared success metrics.
• Work alongside UX/UI and Solution Architects to create and implement evaluation criteria for AI outputs (such as quality, bias, drift, and hallucinations) throughout the design, prototyping, development, and production phases.
• Take ownership of and document data privacy, security, and compliance considerations, including establishing clear guidelines on issues like prompt injection, IP leakage, bias, brand risks, model limitations, failure modes, and established human escalation paths.
• Evaluate cost‑to‑value tradeoffs, incorporating active optimization of token usage, caching strategies, and model routing, while also considering future growth plans.
• Provide strategic insights on roadmaps and intellectual property, identifying reusable patterns that can add value or evolve into platforms, internal tools, and future features based on existing IP.
• Stay at the frontier of AI innovation, exploring emerging models, frameworks, and integrations that enhance our marketing ecosystem.

Requirements

• At least 8 years of experience in machine learning engineering, AI/LLM integration, or applied NLP
• Degree in Computer Science, Engineering, or related field

AI/ML Engineering & Applied NLP

• Demonstrated success in developing and deploying AI/LLM‑based applications within production settings
• Expertise in foundational models (GPT‑4o, Claude, Gemini, Mistral, etc.) and best practices for prompt engineering
• Practical experience with vector databases (pgvector, Pinecone, Weaviate) and Retrieval‑Augmented Generation (RAG) pipelines
• Knowledge of orchestration frameworks such as LangChain or LlamaIndex
• Proficiency in Python programming, including experience with the OpenAI SDK, Hugging Face, or similar AI libraries
• Experience in developing or integrating AI agents, automation systems for creative tasks, or recommendation systems
• Experience in creating custom GPTs/Agents

Web, Backend & Integrations

• Proficient understanding of HTML, CSS (SASS), and JavaScript (jQuery)
• Proficiency with backend technologies such as Node.js, PHP, Java, or Ruby
• Working knowledge of third‑party integrations, APIs, and backend services
• Expertise in creating responsive, accessible, and high‑quality websites and web applications

Product Experimentation & Evaluation

• Comfortable running A/B tests, evaluation frameworks, and feedback loops for continuous AI improvement

Software Delivery & Collaboration Practices

• Proficiency in Agile development methodologies
• Experience with version control systems (e.g., Git)
• Collaboration in cross‑functional teams with designers, developers, and strategists

Marketing Technology

• Experience with marketing automation platforms such as ActiveCampaign, Pardot, or others—including landing pages, automated workflows, template creation, and integrating websites and databases