Kiefer→
ML Team Lead (Sophea AI) at Kiefer in Athens, Attica, Greece
Skills
Job Description
About the company:
Kiefer Tech, the technology arm of Kiefer, leverages over 12 years of engineering heritage from the Green Energy sector to deliver cutting-edge AI, robotics, and enterprise solutions across Greece and the EU. We build sovereign AI infrastructure that keeps data within EU borders, respect privacy, and delivers tangible business impact. Guided by our core values: innovation, quality, and long-term client partnerships, we create enterprise-grade AI infrastructure, the first true Greek Large Language Models, and intelligent automation solutions that empower organizations to thrive. Our strategic collaboration with NVIDIA combines sustainable infrastructure expertise with world-class AI technology, creating an ecosystem that fosters innovation, strengthens Greece’s technological sovereignty, and generates real impact across industries. Join us and help build the AI-powered world of tomorrow.
About the role:
We are looking for an ML Team Lead to join Kiefer Tech and lead our ML Engineering team.
In this role, you will lead the development and continuous improvement of Sophea AI, our Greek-focused Large Language Model. You will work across LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems.
Beyond the model itself, you will help shape the ML Engineering function at Kiefer Tech: define clear ownership, strengthen team workflows, raise technical standards, and ensure complex ML initiatives are delivered with accountability, clarity, and strong execution.
What you will do:
Lead the ML Engineering team working on Sophea AI and set a clear technical direction for its development
Own delivery across core ML workstreams, including LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems
Build a stronger operating model for the ML function: improve workflows, clarify ownership, strengthen decision-making, and remove execution blockers
Turn the ML roadmap into clear priorities, accountable workstreams, and measurable outcomes aligned with product and business goals
Mentor ML engineers, develop team capabilities, and bring stronger engineering practices, tools, and research-driven approaches into the team’s daily work
What you will need:
Proven experience leading ML Engineering teams or complex ML workstreams in high-performance, fast-moving environments
Deep technical background in LLMs, ASR, and production-grade ML systems, including pre-training, training from scratch, fine-tuning, reinforcement learning, model validation, and performance improvement
Strong understanding of inference optimization, model quantization, and serving frameworks such as vLLM, SGLang, NVIDIA Triton, NVIDIA Dynamo, or similar tools
Ability to bring structure into ambiguous environments: clarify ownership, improve workflows, and drive execution across several ML workstreams
High autonomy, strong communication skills, and the ability to follow current AI research and translate relevant tools, methods, and trends into team practice
Nice to have:
Experience with MLOps infrastructure, model serving pipelines, GPU workload management, and experiment tracking
Contributions to open-source ML projects or published research in AI/ML
Familiarity with real-time inference systems or event-driven ML architectures
What is there for you:
Compensation: highly competitive package aligned with AI talent benchmarks across Europe and the US
Ownership: high-impact leadership role with real influence on the ML Engineering function at Kiefer Tech
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, ASR, GPU workloads, and advanced AI product development
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 shape meaningful AI products