AI Engineer

## About the project - We are looking for an AI Engineer who will take end-to-end ownership of AI systems – from prototype to production, including reliability and scalability across LLM pipelines and agentic workflows. - This is a deeply hands-on role with a high level of ownership. One week you may be working in the core product code, another supporting a customer engineering team during an implementation, and another exploring an R&D problem at the edge of what is currently possible. - You will define the business problem, design the solution architecture, and take responsibility for what ultimately reaches the user. Alongside product development and customer implementations, you will also work on R&D topics such as extracting tacit knowledge, automating eval creation, personalizing agents from their traces, and enabling proactive behavior. ## Your responsibilities - Own AI systems end-to-end, from prototype to production, including reliability and scalability across LLM pipelines and agentic workflows - Build AI and data agents, RAG systems, semantic layers, and ontologies that let customers work with their business through Omniviser’s conversational interface - Design production-grade orchestration using tool use, skills and MCP, context engineering, memory and state management, and structured outputs with validation - Implement fallback handling, human-in-the-loop flows, guardrails, and tool permission controls - Prototype new ML and AI ideas in days rather than quarters and turn the strongest concepts into production features - Iterate with Product and Design based on what actually works for users - Build evaluation and monitoring for agents, including tracing, automated evals, RAG evaluation, LLM-as-a-judge, and model benchmarks - Work in an evaluation-driven development model, combining LLMOps and MLOps practices to improve quality, cost, and latency - Support Ops in making the company more AI-native and support Delivery in customer implementations, solution fit, and adoption - Work on open R&D problems such as tacit knowledge extraction, trace-based agent personalization, intent recognition, and proactive product behavior - Use agentic coding tools such as Claude Code, Codex, or Cursor, follow the AI ecosystem, and bring useful new practices into the team - 2–5 years of commercial experience in AI/ML or software engineering, with production systems you have actually shipped - Strong Python skills and experience building and maintaining production backend services and APIs, including async, testing, and CI/CD - Ability to rapidly prototype AI solutions and quickly turn them into customer value - Practical knowledge of agent architectures, including context engineering, tool calling, agent loops, skills, structured outputs, and fallback handling - Experience with orchestration in LangGraph, LangChain, or a custom framework, and with MCP servers or custom tool-calling interfaces - Experience with data agents and production retrieval over customer data, including semantic layers and ontologies; knowledge of PostgreSQL, pgvector, semantic and hybrid search, and BM25 - Ability to build evaluation and validation systems for AI applications, from golden datasets and scenario tests to regression tests and quality gates - Hands-on LLMOps experience, including tracing and observability in Langfuse or MLflow, automated evals, prompt optimization, and production debugging for quality, cost, and latency - Experience deploying and maintaining production applications in the cloud, especially Azure - Daily use of agentic engineering tools such as Claude Code, Codex, Cursor, or GitHub Copilot, together with strong code quality practices: tests, linters, type checkers, documentation, and code review - Very good communication skills in Polish and English and the ability to work with Product and Design teams as well as directly with customer engineers ## Optional - Experience building AI products for customers, especially in a 0→1 phase - Experience deploying AI solutions with measured business value - Experience in a startup or fast-changing product environment - Experience with spec-driven development using agentic engineering tools - Experience securing LLM applications through red teaming, adversarial testing, and anti-jailbreak policy enforcement - Experience optimizing cost and latency through techniques such as context compression and prompt caching ## What we offer - End-to-end ownership of production AI systems, from data and agents to what ultimately reaches the user - A high level of autonomy together with real responsibility for outcomes - Work across core product development, customer implementations, and R&D - Direct influence on how we build, evaluate, monitor, and improve AI systems - Collaboration with Product, Design, Ops, Delivery, and customer engineering teams - An AI-native, fast-moving environment focused on rapid experimentation and measurable results - Compensation: 20 000 – 27 000 PLN net/month + VAT (B2B) ## Benefits - sharing the costs of sports activities - private medical care ## Who we are looking for - Someone with high agency who can take a feature from idea to deployment without waiting for every task to be defined - A product-minded engineer who makes decisions based on real user needs and measurable business outcomes - Someone who combines engineering rigor with pragmatism and can make sensible trade-offs without over-engineering - A strong communicator who can move between technical conversations with engineers and business conversations with stakeholders, in Polish and English - An AI-native early adopter who uses generative AI in everyday work, follows the ecosystem, and shares useful new techniques with the team - OMNIVISER is building an AI-powered Business Operating System that connects all company data to deliver real-time insights, profit projections, and clear decisions. We help leaders cut through data overload and act faster with proactive, context-aware AI that drives measurable business outcomes. - We are currently transitioning from a startup to a scale-up – developing our product, scaling our organization, and preparing for international expansion.