Software Application Engineer managing application development of driveline products for Japanese commercial vehicles. Coordinating project timelines and collaborating with global development teams.
About the role
- Data & Application Engineer developing RAG/LLM components at Leonardo SpA. Responsible for evaluation methodologies, dataset creation, and team collaboration.
Responsibilities
- Progettare e sviluppare componenti di RAG/LLM per risposte sintetiche e spiegabili su documenti interni (prompting, tool/function calling se previsto, gestione multi-turn)
- Definire e implementare metodologie di valutazione (offline/online) per qualità delle risposte: groundedness (aderenza alle fonti), completezza, coerenza, gestione incertezza e copertura del retrieval
- Creare dataset di test e “golden set” di dominio (domande reali, query complesse, casi ambigui/contraddittori) e automatizzare la generazione di report di performance
- Tuning del retrieval ibrido (BM25 + semantic): scelta top-k, pesi, filtri metadati, dedup/reranking e query rewriting per sigle/gergo tecnico
- Sviluppare logiche di planning per query complesse (decomposizione in sotto-query/DAG) e criteri di stop/iterazione del ciclo retrieve→answer→critique
- Collaborare con team di piattaforma per integrazione con inference backend e garantire requisiti di latenza/streaming (SSE) e robustezza in modalità degraded
- Implementare strumenti e pipeline software (Python) per esperimenti riproducibili: tracking configurazioni, versioning prompt/modelli, ablation study, analisi errori e regressioni
Requirements
- Laurea magistrale o PhD in Data Science, Informatica, Ingegneria, Matematica, Statistica o discipline affini
- Ottima programmazione in Python (design modulare, testing, packaging)
- Uso di Git e code review
- Esperienza con NLP/LLM e architetture RAG (embeddings, chunking, retrieval, grounding, citazioni)
- Capacità di costruire evaluation framework: metriche automatiche + human-in-the-loop, analisi qualitativa degli errori, definizione di soglie e criteri di accettazione
- Familiarità con vector search e sistemi di ricerca (OpenSearch/Elasticsearch o equivalenti), BM25, KNN, reranking
- Conoscenza di FastAPI/REST per integrazione servizi e debugging di flussi end-to-end
- Buone basi di statistica sperimentale (A/B test concettuale, significatività, bias/variance), ottimizzazione e interpretazione risultati
- Gradita esperienza con stack containerizzato (Docker/Kubernetes)
Benefits
- Disponibilità a trasferte occasionali
- Gradite certificazioni/esperienza su MLOps
- Osservabilità (logging/metrics)
- Sicurezza applicativa in contesti enterprise/regulated
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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