ML Engineer — Agents & LLMs

Kiefer

2d

About The Position

KIEFER is building an integrated AI ecosystem in Greece, spanning renewable energy infrastructure, AI systems, robotics, and enterprise applications. Founded in 2014, the company has delivered more than 600 MW of energy projects and is developing sovereign AI infrastructure, enterprise AI products, and physical AI systems for Greece and Southeast Europe.

The ML Engineer will improve agent and long-context LLM capabilities behind Kiefer Tech’s AI products. The role involves hands-on work across post-training, data, evaluation, agent design, and inference, with the aim of making multi-step tasks more reliable in production. The engineer will collaborate with ML, Product, and Engineering to improve inference and bring successful experiments into production with clear regression checks.

Requirements

• At least 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 skills, hands-on experience with PyTorch, JAX, TensorFlow, 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed