Python/NLP Developer [#4877]Alteo • Montreal, Montreal (administrative region), CA

Python / NLP Developer [#4877]

Alteo • Montreal, Montreal (administrative region), CA

Il y a plus de 30 jours

Type de contrat

Permanent

Description de poste

Alteo is looking for a Python / NLP Developer for a permanent position based in Montreal.

Responsibilities :

Maintain and implement new features in our search engine.
Develop and maintain a click analytics system to learn user preferences.
Design and improve an intelligent search assistant based on LLM models to assist users with their queries.
Optimize the backend systems responsible for processing data and enriching the AI models used in search.
Collaborate with NLP researchers, data scientists, and domain experts to experiment, test, and continuously improve our systems.
Participate in benchmarking campaigns to measure performance (accuracy, recall, response time, etc.) and optimize systems based on the results obtained.
Explore, design, and evaluate new approaches in NLP, such as prompt optimization, reinforcement learning, or hybrid symbolic / neural approaches.

Profile :

DEC / BAC in IT, Software Engineering or equivalent

3+ years of relevant experience.

Professional experience in machine learning (ML) and natural language processing (NLP), best practices in experimentation and optimization, MLFlow, etc.

Knowledge of modern language models (LLM), how they work, and how they are used via prompting (prompt engineering) or fine-tuning.

Experience in Python (and / or Java) programming in a data processing and AI context.

Proficiency in common tools and libraries : LLM APIs for text completion with function calls (tool use), streaming, “chain of thoughts,” structured outputs, etc.

Understanding of classic and modern information retrieval (IR) methods, including TF-IDF, BM25, dense retrieval, RAG, etc.

Familiarity with collaborative software development tools : Git, CI / CD, containers, etc.

Ability to independently set up reproducible experiments (experimentation, A / B testing, performance logging, etc.).

Experience with large-scale production systems or microservice-oriented architectures (an asset).

Interest in conversational interfaces and intelligent assistants (an asset).

Tech stack : AWS infrastructure : EC2, ECS Fargate, RDS, S3; GitHub, Jenkins, SonarQube, Jira, and Confluence cloud; Windows, Linux, and MacOS work environments.

Alteo est à la recherche d'un Développeur Python / NLP pour un poste permanent basé à Montréal.

Responsabilités :

Maintenir et implanter de nouvelles fonctionnalités dans notre moteur de recherche.

Développer et maintenir un système d’analytique de clics permettant l’apprentissage des préférences utilisateurs.

Concevoir et améliorer un assistant de recherche intelligent basé sur des modèles LLM, pour assister l’utilisateur dans ses requêtes.

Optimiser les systèmes de backend responsables du traitement des données et de l’enrichissement des modèles d’IA utilisés dans la recherche.

Collaborer avec des chercheurs en NLP, des scientifiques de données et des experts du domaine pour expérimenter, tester et améliorer continuellement nos systèmes.

Participer à des campagnes de benchmarking pour mesurer la performance (précision, rappel, temps de réponse, etc.) et optimiser les systèmes en fonction des résultats obtenus.

Explorer, concevoir et évaluer de nouvelles approches en NLP, telles que l’optimisation de prompts, l’apprentissage par renforcement ou les approches hybrides symbolique / neurale.

Profil :

DEC / BAC en TI, Ingénierie logicielle ou l'équivalent

3+ années d’expérience pertinente.

Expérience professionnelle en apprentissage machine (ML) et traitement automatique du langage naturel (NLP), les bonnes pratiques d’expérimentation et d’optimisation, MLFlow, etc.

Connaissance des modèles de langage modernes (LLM), de leur fonctionnement, de leur utilisation via prompting (prompt engineering) ou fine-tuning.

Expérience en programmation Python (et / ou Java) dans un contexte de traitement de données et d’IA.

Maîtrise des outils et bibliothèques courants : APIs LLM de complétion texte avec appel de fonction (tool use), streaming, “chain of thoughts”, sorties structurées, etc.

Compréhension des méthodes classiques et modernes de recherche d’information (IR), y compris TF-IDF, BM25, dense retrieval, RAG, etc.

Familiarité avec les outils de développement logiciel collaboratif : Git, CI / CD, conteneurs, etc.

Autonomie dans la mise en place d’expériences reproductibles (expérimentation, A / B testing, logging des performances...).

Expérience avec des systèmes de production à grande échelle ou des architectures orientées micro‑services (atout).

Intérêt pour les interfaces conversationnelles et les assistants intelligents (atout).

Stack techno : Infrastructure AWS : EC2, ECS Fargate, RDS, S3; GitHub, Jenkins, SonarQube, Jira et Confluence cloud; Environnements de travail Windows, Linux et MacOS.

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