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 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.
What you will do
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.
What you will need
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, 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 have
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.
What is there for you
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.
Summary
Build and evaluate LLM agents and long-context capabilities. Run post-training experiments, improve tool-using multi-step agents, curate training data, and measure failures. Requires 3+ years ML/research engineering, hands-on LLM post-training, agent systems experience, model evaluation, and Python with PyTorch/JAX/TensorFlow.
Responsibilities
Run post-training experiments; improve agent planning, tool use, and error recovery; build benchmarks and verifiers; curate training data; evaluate long-context behavior; collaborate on inference and production regression checks.
Qualifications
3+ years relevant ML or research engineering experience; hands-on LLM post-training; agent systems with tool use and error recovery; model/agent evaluation and training data curation; strong Python and PyTorch/JAX/TensorFlow; model-backed system deployment and LLM inference performance/cost.
Experience requirements
3+ years relevant ML or research engineering experience.