Key Facts

Remote From:

United Kingdom

Full time

English

Hard Skills

Other Skills

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Mentorship
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Systems Thinking
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Communication

Roles & Responsibilities

8+ years of experience in data engineering, AI, ML platforms, or large-scale distributed systems.
Hands-on experience integrating LLMs into production systems (OpenAI, fine-tuning, embeddings, RAG, vector stores, or custom agent orchestration).
Strong understanding of experimentation, model evaluation, and performance tuning.
Ability to work from ambiguity to execution, and strong communication skills to explain tradeoffs and technical strategy.

Requirements:

Design and implement large-scale data systems that feed AI agents and autonomous workflows.
Fine-tune, evaluate, and optimize LLMs for marketing-specific tasks (attribution insights, anomaly detection, summarization, classification, automated recommendations).
Build end-to-end automations using LLMs, internal data, and external signals to eliminate repetitive tasks.
Lead RD initiatives including rapid prototyping, experimentation, model evaluations, productionization, and mentoring data scientists and engineers.

Job description

Rockerbox is building the next generation of marketing intelligence, and we’re looking for someone to help us build the AI systems everyone else just theorizes about. As a Staff AI Data Engineer, you’ll research, design, and architect data systems purpose-built for AI agents, automations, and decisioning engines. You’ll also apply data science and model-tuning techniques to optimize LLMs and solve complex marketing challenges for our clients.

If you enjoy turning high-volume data into clean, powerful systems—and you want your work to drive real business outcomes rather than just dashboards—this role puts you right at the center of the action.

What You’ll Do

Apply bleeding edge AI theory to the design and implementation of large-scale data systems that feed AI agents and autonomous workflows.
Use data science techniques to fine-tune, evaluate, and optimize LLMs for marketing-specific tasks: attribution insights, anomaly detection, summarization, classification, and automated recommendations.
Build end-to-end automations using LLMs, internal data, and external signals to eliminate repetitive human tasks.
Build AI-driven automations that reduce manual work across Rockerbox and unlock new client-facing capabilities.
Design retrieval, orchestration, and memory layers that make our AI agents smarter over time.
Establish best practices for AI data quality, observability, experiments, and safety.
Lead R&D initiatives: rapid prototyping, experimentation, model evaluations, and productionization.
Mentor data scientists and engineers across organization to raise the bar on LLM use company-wide.

What You Bring

8+ years of experience in data engineering, AI, ML platforms, or large-scale distributed systems.
Hands-on experience integrating LLMs into production systems (OpenAI, fine-tuning, embeddings, RAG, vector stores, or custom agent orchestration).
Strong understanding of experimentation, model evaluation, and performance tuning.
You think in systems: storage, retrieval, metadata, reliability, latency, failure modes.
Ability to work from ambiguity to execution — you’re comfortable being the first to figure something out.
Strong communication skills: you can explain tradeoffs, scope decisions, and technical strategy clearly.