AI R&D Engineer – LLM, Prompt & Agent Optimization

Arfitect, founded in 2017 and based at Ege Teknopark, Izmir, is an AI-first custom software and AI consultancy. We build AI-powered enterprise applications for leading companies in energy, manufacturing, aviation and retail, and we develop our own AI SaaS products. Our culture values teamwork, innovation and continuous learning – a place where talent thrives.

We are growing our AI R&D track and looking for an engineer whose job is to find out what actually works – and to make our AI systems smarter, cheaper and more reliable. You will work alongside our current AI R&D engineer and our applied AI engineers. This is a hands-on research and development role: you own experiments, evaluations, and the prompts and agent designs the rest of the team builds on, rather than client delivery deadlines.

• •Track, test and benchmark new models, techniques and tooling as they come out, and give the team a clear verdict on what matters and what doesn't.
• •Design, optimize and maintain our prompts and agent instructions: tool use, planning, memory, multi-agent coordination, failure recovery.
• •Build evaluation sets and experiment harnesses that measure quality, robustness, latency and cost, and use them to iterate.
• •Run experiments on retrieval quality: chunking, embeddings, hybrid search, reranking, context strategies.
• •Design and evaluate safeguards for our prompts and agents – prompt-injection defences, input/output validation, guardrails – and measure their effect on security and reliability.
• •Fine-tune and evaluate open-source models (LoRA/QLoRA) and test local/on-prem deployment (vLLM, Ollama) for clients with data-residency needs.
• •Turn ideas into working prototypes and PoCs; hand validated ones to our applied AI engineers for production, and take their real-world failure cases back into your work.
• •Codify what you learn as reusable assets: prompt libraries, agent skills, evaluation templates, internal guides and demos.

• •At least 1 year of hands-on work with LLMs – prompt engineering, agent development or LLM evaluation – in professional, academic or serious personal projects.
• •Solid Python and the engineering hygiene to keep experiments reproducible: Git, turning notebooks into modules, clean code.
• •Deep practical understanding of how LLMs behave: context windows, tool calling, structured outputs, sampling, common failure modes.
• •Experience with multiple LLM providers and open-source models, and a practical grasp of how they differ in capabilities, pricing, latency, context handling and tool-calling behaviour.
• •Experience with an agent framework (LangGraph, LangChain, LlamaIndex, Semantic Kernel or similar).
• •Hands-on RAG experience and familiarity with vector stores.
• •Strong research skills: you can work through AI papers, technical reports, model architectures and implementation details, and turn relevant findings into practical experiments.
• •An experimental mindset: you form hypotheses, measure, and change your mind when the data says so.
• •Ability to explain findings clearly to engineers and to non-technical stakeholders.
• •English fluency.

• •Curious to the point of obsession about how models and agents behave.
• •Rigorous about measurement: you trust numbers over impressions.
• •Generous with what you learn – you make the whole team better.
• •Comfortable in a field that changes every month.

• •Fine-tuning experience (Hugging Face, PEFT).
• •On-prem LLM serving and inference optimization.
• •LLM observability and evaluation tools (Langfuse, LangSmith, Ragas or similar).
• •Speech and voice models (Whisper, Azure Speech, voice agents).
• •Publications, open-source contributions, or a public portfolio of LLM experiments.

Bachelor's Degree in Computer Science, Software/Computer Engineering, Mathematics or other related field from a four-year College or University.