Senior Machine Learning Engineer - LLM Development and Production

About the company:

Kiefer Tech, the technology arm of Kiefer, leverages over 12 years of engineering heritage from the Green Energy sector to deliver cutting-edge AI, robotics, and enterprise solutions across Greece and the EU. We build sovereign AI infrastructure that keeps data within EU borders, respect privacy, and delivers tangible business impact. Guided by our core values: innovation, quality, and long-term client partnerships, we create enterprise-grade AI infrastructure, the first true Greek Large Language Models, and intelligent automation solutions that empower organizations to thrive. Our strategic collaboration with NVIDIA combines sustainable infrastructure expertise with world-class AI technology, creating an ecosystem that fosters innovation, strengthens Greece’s technological sovereignty, and generates real impact across industries. Join us and help build the AI-powered world of tomorrow.

About the role:

We are looking for a Senior Machine Learning Engineer to join Kiefer Tech and strengthen our ML Engineering team in Poland.

In this role, you will work on the development and continuous improvement of Sophea AI, expanding our LLM capabilities across European languages, with a particular focus on Polish. You will be deeply involved in LLM pre-training, training from scratch, fine-tuning, model evaluation, inference optimization, and production-grade ML systems.

This is a hands-on engineering role for someone who has already worked directly with language models and understands how to improve their quality, performance, and reliability in real production environments.

Important: this role requires strong practical experience with LLM development. Classical ML, computer vision, basic RAG, or high-level AI tools alone will not be enough for this position.

What you will do:

• Work on Sophea AI across LLM pre-training, training from scratch, fine-tuning, evaluation, and continuous model improvement
• Build production-grade ML pipelines for inference, serving, deployment, monitoring, and model lifecycle management
• Optimize model performance in production, including latency, throughput, cost efficiency, quantization, and GPU workload usage
• Work with datasets, experiments, benchmarks, and evaluation methods to improve language model quality and domain-specific performance
  
  

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What you will need:

• Strong hands-on experience with LLMs, including pre-training, training from scratch, fine-tuning, evaluation, and performance improvement
• Strong ML engineering background, including Python, PyTorch, Docker, and production ML practices
• Experience with model serving, inference optimization, quantization, GPU workloads, and frameworks such as vLLM, SGLang, NVIDIA Triton, TensorRT, TGI, or similar tools
• Ability to build production-grade ML systems, not only research prototypes, scripts, basic RAG applications, or high-level AI integrations
• Native-level Polish language proficiency
  
  

Nice to have:

• Experience with ASR systems, speech models, or speech-to-text pipelines

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• Experience working with non-English language models, multilingual models, or low-resource language adaptation
• Experience with MLOps infrastructure, experiment tracking, model serving pipelines, and GPU workload management
• Contributions to open-source ML projects or published research in AI/ML
  
  

What is there for you:

• Compensation: competitive package aligned with talent benchmarks
• Impact: hands-on role working on Sophea AI, one of the most ambitious Greek-focused AI products in the market
• Work format: remote work option, with relocation support available for candidates open to working from our Athens office
• AI-native environment: real challenges across LLMs, training, fine-tuning, inference optimization, GPU workloads, and production AI systems
• NVIDIA ecosystem: access to related conferences, certifications, internal knowledge sharing, and advanced AI infrastructure through Kiefer’s strategic collaboration
• Culture: engineering-first, high autonomy, low bureaucracy, and space to build meaningful AI products