About SynlogIQ

SynlogIQ helps enterprises become AI-ready. We fix the data and engineering foundations — platform engineering, data engineering, the Microsoft stack, agentic AI, and custom software — so AI actually works in production, not just in a demo.

SynlogIQ engineers come from across Southeast Europe — eight countries that share your working day, carry a register that holds up in the boardroom, and draw on a craft tradition that runs deep.

Engineering talent, embedded

Alongside our project work, we place senior engineers directly inside client engineering teams — people who own their work, think in systems, and raise the standard of the teams they join. The role below is one of those engagements: you would be embedded with a client’s engineering team as part of a long-term contractor partnership through SynlogIQ.

About the role

We’re looking for an AI Engineer to build production-grade AI applications for an enterprise client. Your focus won’t be training foundation models. Instead, you’ll design and build intelligent products that combine LLMs, agentic workflows, APIs, tools, and external systems into reliable, scalable, production-ready solutions — well beyond simple prompt-and-response.

Working with product managers, software engineers, and data scientists, you’ll ship AI-powered features that create measurable business value.

What you’ll do

Design and build applications powered by LLMs (OpenAI, open-weight models)
Build agentic workflows with planning, memory, and tool use
Develop prompt-engineering and evaluation strategies
Integrate AI with APIs, databases, vector stores, and external services
Improve the reliability, performance, and observability of production AI systems
Experiment with new models and frameworks to keep improving solutions

What we’re looking for

2–5 years in software engineering, machine learning, or applied AI
Strong Python (async, APIs, data pipelines)
Experience building production-grade LLM applications
Familiarity with LangChain, LangGraph, or similar frameworks
Solid grasp of prompt engineering, tokenisation, and model limitations
Experience with cloud platforms, APIs, and modern engineering practices (testing, versioning, CI/CD)
Comfortable debugging non-deterministic, unpredictable AI systems
Strong analytical and problem-solving skills

Nice to have

Open-source LLMs and model hosting (vLLM, Hugging Face)
Vector databases
AI evaluation frameworks (automated and human-in-the-loop)
Optimising latency, cost, and reliability of LLM systems
Voice AI (text-to-speech, speech-to-text, orchestration)
Knowledge of AI safety and security
Clear communication of complex AI behaviour to non-technical stakeholders

Why join

Build real AI products, not demos
Solve hard, real-world engineering problems
Work with experienced engineers and AI specialists
Influence product direction with modern AI technology, fully remote

AI Engineer