Senior LLM Engineer: Text & Reasoning LLM / NLU
O
OmiliaConversational AI
Greece. Spain. Portugal. Italy. BulgariaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonPyTorchFastAPIPrompt Engineering
Requirements
- 5+ years in applied LLM/ML/NLU/NLP, with ownership of production ML systems at scale
- Strong hands-on skills in Python
- Strong hands-on skills in PyTorch
- Strong hands-on skills in HuggingFace Transformers
- Deep experience with LLMs: fine-tuning, distillation, prompt engineering, evaluation, and deployment (especially small/efficient models)
- Solid foundation in NLU: intent classification, entity extraction, etc.
- Experience with model serving infrastructure (e.g., Triton Inference Server, vLLM, TGI, FastAPI)
- Experience with cloud ML infrastructure (AWS SageMaker, Bedrock, or equivalent)
- Proven architectural decision-making and technical ownership across services/products
- Ability to break down ambiguous problems and drive actionable plans
- Excellent communication skills for both technical and non-technical audiences
Responsibilities
- Lead the technical development and continuous improvement of Omilia’s proprietary LLM and NLU service portfolio
- Ensure technical correctness, system quality, and delivery monitoring for mission-critical AI services
- Hold final technical authority over all LLM/NLU services, including entity/intent classification, specialized LLMs, and agentic orchestration
- Ensure production stability, performance, and compliance (including PCI/PII) across the LLM/NLU domain
- Commit to delivery dates, drive features from design through deployment, and proactively flag risks
- Resolve technical ambiguity, structure loosely defined requirements, and make architectural decisions independently
- Lead the most complex, ambiguous, or cross-cutting features, including model research, agentic reasoning, and inference server development
- Directly influence the quality and reliability of AI services serving millions of customer interactions in regulated industries
- Guide and mentor mid-level and junior engineers through code reviews, pairing, and knowledge transfer; drive alignment between Product, Architecture, and Engineering
- Lead research and experimentation on new model architectures, training strategies, and evaluation methodologies for LLM/NLU
- Design, develop, fine-tune, and evaluate specialized LLMs for Concierge and Task Agents
- Develop and optimize ML pipelines for training, evaluation, and deployment (AWS SageMaker)
- Architect and maintain inference servers, ensuring low latency and high reliability
- Implement and evolve closed-loop self-learning systems for continuous model improvement
- Drive benchmarking, experiment reproducibility, and documentation quality
- Ensure compliance with data privacy standards throughout the ML lifecycle
- Mentor and support the growth of team members; share expertise via tech talks and guides
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