Senior AI Engineer – Search, Ranking & Applied ML

Talent Quanta is seeking a Senior AI Engineer – Search, Ranking & Applied ML fluent in English, to offer his/her services as an expert who will be based in Luxembourg , Luxembourg. The work will be carried out either in the company’s premises or on site at customer premises. In the context of the first assignment, the successful candidate will be integrated in the Development team of the company that will closely cooperate with a major client’s IT team on site.

Technical Requirements

• Strong expertise in Python
• Advanced experience with machine learning systems in production
• Strong experience with Learning-to-Rank (LambdaMART)
• Hands-on experience with: XGBoost, LightGBM (ranking objective preferred)
• Strong understanding of: feature engineering for ranking systems, structured + semantic feature blending, model evaluation (NDCG, MRR, Precision@K, Recall@K)
• Experience designing ML systems without historical click data

Search & Retrieval Requirements

• Experience building or working with: hybrid search systems (BM25 + embeddings), vector search architectures
• Practical experience with Vespa or similar search infrastructure
• Strong understanding of: embedding generation pipelines, similarity search, retrieval optimization
• Experience balancing recall vs precision trade-offs

LLM & Weak Supervision Requirements

• Experience using LLMs for: label generation, pairwise preference modeling, weak supervision workflows
• Experience with: LangGraph, LangSmith or LangFuse
• Practical knowledge of transformer fine-tuning
• Experience with the Hugging Face ecosystem
• Ability to design evaluation loops for LLM-generated supervision

Computer Vision & Document Understanding Requirements

• Experience building or improving OCR pipelines
• Familiarity with modern document understanding approaches: layout-aware models, transformer-based OCR systems
• Experience working with CV parsing and structured data extraction
• Ability to improve extraction quality beyond traditional Tesseract pipelines
• Understanding that parsing quality directly impacts ranking quality

System & Infrastructure Requirements

• Experience designing ML systems for: low latency, production reliability, cost efficiency
• Understanding of model serving architectures
• Experience building experimentation pipelines
• Ability to design evaluation frameworks from scratch
• Experience working with structured ranking signals: skills, seniority, location, experience alignment

Product & Design Mindset

• Strong product awareness - understands business impact of ranking quality
• Ability to design models aligned with recruiter workflows
• Thinks beyond model accuracy to: trust, interpretability, explainability
• Comfortable collaborating with frontend engineers to expose interpretable signals
• Makes pragmatic decisions instead of chasing unnecessary SOTA performance

Startup & Ownership Requirements

• Thrives in high-paced startup environments
• High autonomy, can design and ship systems independently
• Comfortable working with incomplete or evolving data
• Strong ownership of ranking quality end-to-end
• Able to define experiments and success metrics without predefined frameworks
• Balances research thinking with delivery speed
• Proactive, execution-focused, and highly accountable

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