AI Engineer

About the Role

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 — including R&D topics such as extracting tacit knowledge, automating eval creation, personalizing agents from their traces, and enabling proactive behavior.

Key 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

Required Qualifications

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

Nice to Have

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

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

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
20 000 – 27 000 PLN net/month + VAT (B2B)