Generative AI Engineer

About us Zetaris is the world's leading data harness for AI - the open, vendor-agnostic layer that harnesses an organization's data engines and AI models so every agent, analyst, and application can build on enterprise data instantly. Zetaris lets teams query, govern, and activate their data in place, across every source and system they own, without data movement, ETL pipelines, or proprietary lock-in. By harnessing underlying engines such as Spark, Presto, and Trino, Zetaris delivers governed, real-time access to data wherever it lives - with full sovereignty over security, cost, and compliance. As enterprises face mounting pressure to deliver AI ROI on fragmented, distributed data estates, Zetaris provides the open data infrastructure that turns that ambition into measurable outcomes. Role Overview An AI Engineer who is passionate about designing, building, and productionising the AI and agentic capabilities that power AgentFlow and the wider Zetaris platform. You will build LLM-powered features - including retrieval-augmented generation, tool-using agents, and model orchestration - that let customers query, reason over, and act on enterprise data safely and reliably. This role is responsible for the full lifecycle of AI features: from architecture and prompt/context design, through evaluation and guardrails, to deployment and ongoing monitoring in production. You will work closely with Software Engineering, Product, and Data Engineering teams to embed AI capabilities deeply into the Zetaris platform. Basic Zetaris platform knowledge: Basic knowledge of the Zetaris platform is important for this role. As part of your application, please register and navigate the platform at https://www.zetaris.com/cloud. No deep product knowledge is required - we're interested in seeing how you navigate and apply the platform based on your understanding. Responsibilities Design, build, and productionise AI and agentic capabilities across the Zetaris platform, including: Design and build LLM-powered features for AgentFlow, including agent orchestration, tool use, and multi-step reasoning workflows. Design and implement retrieval-augmented generation (RAG) pipelines over structured and unstructured enterprise data. Develop and maintain prompt and context engineering strategies to drive accurate, reliable model behaviour across use cases. Integrate and evaluate multiple LLM providers and models (e.g. Anthropic, OpenAI, open-source models), including deterministic routing between them based on task, cost, and performance. Build evaluation frameworks and test suites to continuously measure model accuracy, safety, latency, and cost in production. Implement guardrails, safety checks, and human-in-the-loop mechanisms to ensure AI features behave reliably and within policy. Work with vector databases and embedding models to support semantic search and retrieval at scale. Collaborate with Data Engineering to ensure AI features have governed, high-quality access to underlying data sources via the Zetaris query engine. Monitor deployed AI features in production, diagnosing and resolving issues related to model behaviour, latency, or cost. Stay current with the rapidly evolving AI/LLM landscape and advocate for adoption of new techniques, models, and tooling where they add customer value. Document architecture, prompts, evaluation results, and known limitations to support knowledge-sharing and responsible AI practices. Work effectively within Agile cross-functional teams and contribute to continuous improvement of engineering practices. Attributes Strong understanding of LLM fundamentals, including transformer architectures, prompting, fine-tuning, and inference trade-offs. Hands-on experience building production applications on top of LLM APIs (e.g. Anthropic Claude, OpenAI, or similar). Experience designing and building retrieval-augmented generation (RAG) systems, including chunking, embedding, and retrieval strategies. Experience with agentic frameworks and tool-use/function-calling patterns for LLM-driven workflows. Experience with vector databases and embedding/semantic search techniques. Proficiency in Python and familiarity with common AI/ML tooling and libraries. Experience building evaluation harnesses to measure model accuracy, safety, and regression across releases. Understanding of AI safety, guardrails, and responsible AI practices in production systems. Experience with cloud platforms such as Azure, AWS, or GCP, including AI/ML services. Working knowledge of data engineering concepts (pipelines, query engines, structured/unstructured data) to integrate AI features with real data sources. Experience with version control, CI/CD, and MLOps/LLMOps practices for deploying and monitoring AI features. Experience working in fast-paced Agile environments. Strong troubleshooting and analytical problem-solving skills. Strong communication and collaboration skills. Up to date with the fast-moving AI/LLM landscape and best practices. Behavioural & Soft Skills Collaborates effectively within Agile, cross-functional teams to achieve shared goals. Demonstrates strong analytical and problem-solving abilities with a practical approach to challenges. Adapts quickly to shifting priorities and thrives in a fast-paced, evolving environment. Takes ownership of tasks, works independently, and consistently delivers high-quality outcomes. Communicates complex ideas clearly and confidently with both technical and non-technical audiences. Shows curiosity and a commitment to continuous learning, exploring new technologies and best practices. Balances innovation with pragmatic execution to deliver reliable, scalable solutions. Acts with integrity, professionalism, and accountability in all interactions and decisions. Contributes to a supportive, knowledge-sharing, and growth-oriented team culture. Maintains a proactive, positive, and results-focused mindset, even under pressure. Why you will love working with Zetaris Be at the forefront of an exciting international expansion backed by a global co-investment partnership. Shape the architecture and commercial direction of a growing software company. Work in a collaborative, agile environment where your ideas directly influence success. Competitive remuneration with performance incentives and potential for equity participation.