About the role

AI Architect designing and evolving Trader Interactive’s AI platforms and architectures. Focus on connecting data, machine learning, and large language models for marketplace solutions.

Responsibilities

Designing and evolving Trader Interactive’s AI platforms and reference architectures
Connecting data, machine learning, and large language models into real products that help buyers and sellers of RVs, boats, powersports, airplanes, trucks, and equipment
Evaluating and selecting AI tooling and platforms (LLMs, vector stores, feature stores, orchestration, observability)
Providing technical mentorship to data scientists and engineers through design reviews, pairing on critical paths, and clear architectural documentation
Continuously scanning the AI landscape and recommending technologies that improve product performance, developer velocity, or cost
Partnering with Security, Legal, and Compliance to define and enforce responsible AI guardrails
Designing AI solutions that run reliably in production
Implementing the systems and processes needed to manage model lifecycle
Collaborating across Product, Engineering, Analytics, and Sales to turn fuzzy ideas into concrete AI use cases

10+ years of experience building software, data, or distributed systems
Strong experience working with cloud-native architectures (AWS preferred) and modern data platforms (e.g., Snowflake/Redshift/BigQuery, streaming/queues, APIs, microservices)
Deep understanding of applied machine learning and/or LLM-based systems: data preparation, feature engineering, training and inference patterns, evaluation, and monitoring in production
Hands-on familiarity with at least some of the following: vector databases, RAG patterns, prompt engineering/guardrails, model registries, feature stores, workflow orchestration, and observability for ML/AI
Relevant training and/or certifications in cloud, data, or ML/AI architecture (or equivalent real-world track record of shipping AI systems at scale)
Ability to move comfortably between high-level architecture (C-suite and product discussions) and low-level detail (API contracts, data models, performance constraints)
Clear, concise communication style — able to explain complex AI systems to non-technical stakeholders without overselling or hand-waving.