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Salary
$90k – $179k per year
Location
Hybrid (Athens, Greece)
Seniority
Senior
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Jun 10, 2026. KIEFER scores A on the Alion truth index.

Overview
Company
Impact
Profile match
KIEFER is an energy and advanced technology group headquartered in Athens, Greece. The company builds and operates renewable energy projects, principally wind, and runs a technology arm developing Sophea AI, a family of Greek language models, along with robotics and sovereign GPU cloud infrastructure. It is establishing Greece's first sovereign AI data centre, powered by renewable generation, in partnership with NVIDIA.

About the company:

KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe.

About the role:

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

In this role, you will work on the development and continuous improvement of Sophea AI, our Greek-focused Large Language Model. 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

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

Nice to have:

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

  • 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

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