Backend) Software Engineer

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 take on a hybrid role that combines people management with hands-on engineering. You will be responsible for the growth and effectiveness of the team, and you will also write code, review pull requests, and shape technical designs alongside them. On this team, leads stay close to the work. Hands-on contribution is a core part of the role, not something you step back from as the team grows.

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.

People leadership Lead and develop the GEO backend team. This covers hiring, regular 1:1s, performance conversations, and supporting each engineer’s growth. Own delivery with Product. Shape priorities, make sure commitments are realistic, and remove blockers so the team ships steadily. Build the engineering culture of the team: shared standards, constructive code reviews, knowledge sharing, and collective ownership of quality. Hands-on engineering Contribute regularly to design, code, and reviews as an active member of the team, not only as a reviewer or approver.

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.

Proven experience leading an engineering team with genuine people responsibility (hiring, 1:1s, performance, and delivery) while continuing to contribute code and technical design. Motivation to stay hands-on as a lead, balancing management responsibilities with regular engineering work. Strong proficiency in Kotlin or Groovy; proven coding skills in other JVM languages (such as Java or Scala) are a plus. Solid experience with our core stack and architecture:
Spring Boot, Hibernate, JUnit, MySQL, Elasticsearch, Docker, AWS and OpenAPI, applied within microservice and event-driven systems. 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 Python and LLM orchestration frameworks such as Lang Chain or Lang Graph. Experience with prompt management, retrieval-augmented generation, embeddings, vector search, or LLM evaluation frameworks. No experience training foundation models is required; this is a backend engineering and engineering leadership role.