ML Engineer / AI Research Engineer
Free Pro — Iliad Group
Applied AI research and engineering within an industrial R&D environment, from experimental investigation and technical validation to the design of production-oriented AI systems.
LLM Systems & Knowledge
Design and experimentation around enterprise LLM architectures, including RAG, hybrid retrieval, vector search, knowledge graphs, graph-based memory and agent workflows.
Evaluation & Research
Development of reproducible experimental frameworks for embedding models, retrieval systems and LLM pipelines, including controlled benchmarks, synthetic and adversarial datasets, ablation studies and automated evaluation.
AI Engineering
Integration of validated approaches into maintainable AI systems, including multimodal pipelines, service architectures, deployment workflows and production-oriented engineering practices.
Selected contributions
- Designed hybrid retrieval architectures combining semantic, lexical and graph-based information retrieval.
- Built evaluation pipelines for embeddings, retrieval components and end-to-end LLM systems.
- Developed synthetic and adversarial evaluation datasets for robustness analysis and controlled experimentation.
- Investigated LLM-based evaluation mechanisms for generation quality, structured outputs and system-level behavior.
- LLM Systems
- Retrieval / RAG
- Agents
- Representation Learning
- Knowledge Graphs
- Evaluation
- Multimodal AI
- MLOps
Technical environment Python · Transformers · Qdrant · Neo4j · Docker