GEO stands for Generative Engine Optimization. The GEO team builds products that help multi-location brands understand and improve how they are represented, cited, and recommended across AI-powered search and answer engines such as ChatGPT, Gemini, Perplexity, and Google AI Overviews.
We are building backend capabilities that collect and analyse signals from rapidly evolving AI platforms, measure brand visibility, and turn complex data into actionable recommendations for our customers.
As Backend Engineering Lead for Uberall's GEO team, you will combine hands-on technical leadership with responsibility for the growth and effectiveness of the engineering team.
You will shape the architecture behind our AI-search products, lead the delivery of new capabilities, and help the team navigate a fast-moving and technically ambiguous domain. Working closely with Product, Engineering, and other stakeholders, you will translate customer needs and emerging developments in generative AI into reliable, scalable, and maintainable backend solutions.
Guide the design and operation of backend systems that integrate with LLMs and AI-powered search platforms.
Establish robust approaches for evaluating the quality and consistency of AI-generated results.
Ensure that LLM-enabled capabilities are observable, testable, cost-efficient, and resilient to changing models and external providers.
Help the team manage the particular challenges of generative AI systems, including non-deterministic behaviour, latency, rate limits, data quality, and graceful degradation.
Stay informed about relevant developments in LLMs and AI search, assessing them pragmatically rather than adopting technology for its own sake.
Hands-on experience building or operating production systems that use LLMs, generative AI, or AI-powered external services.
An understanding of how to evaluate and monitor LLM-enabled features beyond traditional deterministic testing.
Experience designing reliable integrations with third-party APIs and managing concerns such as cost, latency, rate limits, observability, and provider failure.
The ability to distinguish between an effective AI use case and one better solved with conventional software.
Experience with prompt management, retrieval-augmented generation, embeddings, vector search, or LLM evaluation frameworks is beneficial but not essential.
No experience training foundation models is required; this is primarily a backend engineering and technical leadership role.
