ML Engineer — Agents & LLMs

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. We are looking for an ML Engineer to improve the agent and long-context LLM capabilities behind Kiefer Tech’s AI products.

You will work hands-on across post-training, data, evaluation, agent design, and inference, with the goal of making multi-step tasks more reliable in production.

• Run post-training experiments using supervised fine-tuning, preference optimization, reinforcement learning, or other suitable methods.
• Improve how agents plan, use tools, manage execution, and recover from errors.
• Build benchmarks, verifiers, and diagnostic tools to measure agent performance and identify failures.
• Generate and curate training data, including agent trajectories and difficult cases from target workflows.
• Evaluate and improve long-context behavior, memory and retrieval interactions, and reliability over multi-step tasks.
• Work with ML, Product, and Engineering to improve inference and bring successful experiments into production with clear regression checks.

• 3+ years of relevant ML or research engineering experience, with clear technical ownership and measurable results.
• Hands-on LLM post-training experience in at least one area: supervised fine-tuning, preference optimization, reinforcement learning, or a comparable method.
• Experience building agent systems involving tool use, multi-step execution, and error recovery.
• Strong skills in model and agent evaluation, failure analysis, and the creation or curation of training data.
• Strong Python, hands-on experience with PyTorch, JAX, Tensor Flow, or a similar framework, and solid software engineering skills.
• Experience improving or deploying model-backed systems, with an understanding of LLM inference performance and cost.
• Reward modeling or verifier-based training.
• Long-context training, memory systems, retrieval, or context compression.
• Distributed training or high-performance LLM serving.
• Experience delivering agents for complex enterprise, coding, or research workflows.

• Compensation:
  Competitive package aligned with talent benchmarks.
• Impact:
  Hands-on work shaping AI capabilities for Kiefer Tech’s products and real-world applications.
• AI-native environment:
  Real challenges across model development, evaluation, and production AI systems.
• Culture:
  Engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products.