Senior ML Engineer, LLM

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🕒 June 22

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KIEFER

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

🏗️ Construction

🏥 Healthcare

📦 Logistics

Construction • Healthcare • Logistics

KIEFER is an energy technology company focused on developing and delivering technologies that improve the production, storage, distribution, or efficiency of energy systems. The company works at the intersection of engineering and energy innovation to provide solutions for utilities, industrial operators, and other energy stakeholders aiming to reduce costs, increase reliability, and lower environmental impact.

📋 Description

• 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

🎯 Requirements

• 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

🏖️ Benefits

• 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