ML Engineer — Agents & LLMs

Kiefer; Ohio; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Autonomy and engineering-first culture

Hands-on AI product impact

Job summary

KIEFER is seeking an ML Engineer to advance 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.

Join a fast-moving, AI-native environment with room to shape models, tooling, and deployments while maintaining high autonomy and rigorous regression checks.

Qualifications

• 3+ years of ML or research engineering with demonstrable results.
• LLM post-training experience in supervised fine-tuning, RL, or similar.
• Experience building agent systems with tool use and multi-step execution.
• Strong model/agent evaluation and data curation skills.
• Proficient Python and ML frameworks; solid software engineering.
• Experience with LLM inference performance and cost.

Responsibilities

• Run post-training experiments across methods (SFT, RLHF, etc.).
• Improve agent planning, tool use, execution, and error recovery.
• Build benchmarks, verifiers, and diagnostic tools.
• Generate and curate training data including trajectories.
• Evaluate long-context behavior and memory retrieval.
• Collaborate to production with regression checks.

Skills

ML experience

LLM post-training

Agent systems

Python

ML frameworks

Tools

PyTorch

JAX

TensorFlow

Python tooling

Job description

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.