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MLOps Engineer

🌍 Qui sommes-nous ?

 

Safran.AI (anciennement Preligens) propose des solutions d’intelligence artificielle pour analyser les images satellite Ă  haute rĂ©solution, les flux vidĂ©os FMV (full motion video) et les signaux acoustiques. Nos solutions sont dĂ©ployĂ©es au service de l’aĂ©ronautique, la dĂ©fense et les applications gouvernementales.


La sociĂ©tĂ© dĂ©veloppe depuis 2016 des algorithmes et logiciels complexes permettant d’analyser, de dĂ©tecter et d’identifier automatiquement des objets prĂ©sentant un intĂ©rĂȘt militaire, Ă  partir de donnĂ©es d’origine commerciale ou gouvernementale.


Depuis son intĂ©gration au groupe Safran en septembre 2024, Safran.AI contribue Ă©galement Ă  la transformation du groupe, en appliquant les solutions d’IA aux domaines de l’industrie 4.0. À titre d’exemple, l’analyse d’images automatisĂ©e par l’IA peut assister les contrĂŽleurs en charge de l’inspection de piĂšces critiques en les aidant Ă  dĂ©tecter les anomalies Ă©ventuelles Ă  partir de clichĂ©s numĂ©riques.


Chez Safran.AI, l'innovation et la crĂ©ation d'un monde plus sĂ»r sont au cƓur de notre ADN. En nous rejoignant, vous travaillerez avec des Ă©quipes passionnĂ©es et pluridisciplinaires (ingĂ©nieurs, chercheurs, dĂ©veloppeurs
) parmi les plus talentueux du secteur, tous animĂ©s par une passion commune pour l'excellence technologique. Nous offrons un environnement de travail stimulant, oĂč la crĂ©ativitĂ© et la prise d'initiative sont encouragĂ©es, et oĂč chaque idĂ©e compte.


😎 Votre mission, si vous l’acceptez


L’équipe AI Engineering dĂ©veloppe un ensemble d’outils internes de MLOps, Ă  destination des data scientists, visant Ă  accĂ©lĂ©rer le dĂ©veloppement et la mise en production de modĂšles d’IA, “l’AI Factory”. Toutes les Ă©tapes de dĂ©veloppement sont couvertes, de l’ingestion des donnĂ©es au dĂ©ploiement final : construction et stockage des jeux de donnĂ©es, entraĂźnement de modĂšles, Ă©valuation et packaging de modĂšles, tout en garantissant la traçabilitĂ©.


La plateforme IA a Ă©tĂ© historiquement tournĂ©e vers le traitement d’images satellites Ă  destination d’équipes internes. Nous prĂ©voyons une forte croissance du nombre de modĂšles dĂ©ployĂ©s, une diversification des types de donnĂ©es, et une extension de notre base utilisateurs, y compris hors de Safran.AI.


En tant que MLOps Engineer, vous travaillerez notamment Ă  :

→ Garantir la maintenabilitĂ© et l'Ă©volutivitĂ© des diffĂ©rents composants de notre plateforme de MLOps : en vous appuyant sur les services de notre fournisseur de cloud, mais aussi dans des contextes on-premise, en lien avec nos activitĂ©s DĂ©fense


Et à adapter notre stack technique pour répondre aux nouveaux besoins et anticiper les futurs besoins utilisateurs en repoussant les limites des systÚmes existants :

→ Accompagner l’augmentation du nombre de modĂšles produits en amĂ©liorant la modularitĂ© et la robustesse des outils proposĂ©s aux data scientists

→ DĂ©velopper des solutions pour accĂ©lĂ©rer la mise en production de nos modĂšles : rĂ©-entraĂźnement de modĂšles en continu, plans d’expĂ©rience automatisĂ©s, optimisation du packaging

→ Garantir l’exploitabilitĂ© de nos modĂšles en contexte contraint (accĂšs restreint Ă  l’infrastructure des clients) - amĂ©liorer le serving des modĂšles en production (serveur d’infĂ©rence, gestion multi-GPU)

→ AmĂ©liorer nos outils pour profiler et benchmarker nos modĂšles

→ Effectuer une veille technologique de l’état de l’art pour amĂ©liorer notre stack.


De façon transversale, vous aurez l’occasion d’influer sur les choix techniques et les orientations de notre stack, de peser sur notre mĂ©thodologie de travail, et de partager votre expĂ©rience aux autres membres de l’équipe via du pair-programming, des prĂ©sentations ou du mentorat, le tout dans un contexte de large Ă©quipe d’IA produisant des solutions partant en production, Ă  l’échelle.


Attention : la capacité à obtenir une habilitation Défense est obligatoire pour ce poste.


🎯 Votre profil


Vous avez la volontĂ© de participer Ă  la construction d’une plateforme MLOps et donc une curiositĂ© autour des enjeux et spĂ©cificitĂ©s de la mise en production de solutions d’IA.

Cette plateforme se doit d’ĂȘtre axĂ©e sur l'apport de valeur Ă  ses utilisateurs, grĂące Ă  un code sĂ©curisĂ©, testĂ© et mettant en oeuvre des choix pragmatiques et sains.


En termes de compétences, vous disposez :

→ Au moins 3 ans d’expĂ©rience sur des problĂ©matiques d’IA en production

→ D’une trĂšs bonne connaissance de Python et des bonnes pratiques en dĂ©veloppement logiciel

→ Une forte comprĂ©hension des bases de donnĂ©es et de la conteneurisation

→ D’une expĂ©rience sur les problĂ©matiques de dĂ©ploiement, monitoring, observabilitĂ©

→ D'une bonne capacitĂ© Ă  partager et dĂ©fendre une vision technique, faire preuve de pĂ©dagogie

→ D’une posture user-centric, en pensant avant tout aux besoins des utilisateurs

→ D'une bonne capacitĂ© Ă  vulgariser, travailler en Ă©quipe, et faire preuve de proactivitĂ©

→ IdĂ©alement, vous avez une expĂ©rience en serving de modĂšles (ex : Triton, Tensorflow Serving, TorchServe)


Si vous ne remplissez pas 100% des critĂšres ci-dessus, pas de panique, vous pouvez nous indiquer les raisons pour lesquelles vous pensez tout de mĂȘme ĂȘtre un bon candidat pour ce rĂŽle !

 

💙Pourquoi rejoindre Safran.AI ?

 

Rejoindre Safran.AI, c’est rejoindre une entreprise de passionnĂ©s, pionniĂšre dans son domaine pour travailler sur des technologies innovantes et rĂ©soudre des problĂ©matiques techniques complexes Ă  l’état de l’art. Notre volontĂ© de placer l’humain au cƓur de nos activitĂ©s se traduit par un fort esprit d’équipe et d’entraide.


Rejoignez-nous et crĂ©ez aujourd’hui la sĂ©curitĂ© de demain !

 

💰Ce que nous offrons

 

→ Environnement remote-friendly avec jusqu’à trois jours de tĂ©lĂ©travail par semaine.

→ Jeudis aprĂšs-midi dĂ©diĂ©s aux projets personnels et au dĂ©veloppement des compĂ©tences.

→ Un salaire compĂ©titif et Ă©quitable dans l’organisation.

→ Un minimum de 33 jours de congĂ©s par an.

→ CongĂ© second parent Ă©gal au congĂ© post-naissance (10 semaines pour le premier enfant).

→ Programmes de dĂ©veloppement professionnels et personnels sur-mesure.

 

đŸ’Ș Notre process de recrutement

 

→ Un Ă©change de 45 minutes avec un recruteur pour en apprendre plus sur vous, vos attentes et vous donner plus de dĂ©tails sur la vie chez Safran.AI.

→ Un Ă©change de 45 minutes avec votre futur manager ou une personne de son Ă©quipe afin de vous permettre dĂšs le dĂ©but de rencontrer vos futurs collaborateurs et de rentrer dans la technique de votre mĂ©tier !

→ RĂ©alisation d’un cas pratique que vous serez invitĂ©(e) Ă  prĂ©senter Ă  un panel composĂ© de votre futur manager ainsi que d’un ou deux pairs

→ Un entretien avec le Vice-PrĂ©sident de l’organisation que vous rejoindrez

 

Notre process de recrutement dure gĂ©nĂ©ralement entre 20 et 30 jours selon vos disponibilitĂ©s. En cas de deadlines serrĂ©es, nous savons aussi mettre le turbo pour ne pas vous faire attendre ! 🚀

 

Toutes nos offres sont ouvertes aux personnes en situation de handicap

Safran.AI s’engage à traiter chaque candidature de maniùre objective et inclusive



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What You Should Know About MLOps Engineer, Safran.AI

At Safran.AI, located in the beautiful city of Paris, we are searching for an MLOps Engineer to join our innovative team. Our company specializes in crafting intelligent solutions that harness artificial intelligence for analyzing high-resolution satellite images and multimedia data. As an MLOps Engineer, you'll play a pivotal role in developing internal tooling aimed at streamlining the AI model lifecycle for our talented data scientists, helping us shape our cutting-edge AI Factory. Your expertise will ensure that our MLOps platform remains maintainable and scalable, enabling us to tackle an increasing variety of models and data types. Working within our diverse and passionate multidisciplinary teams, you’ll have the chance to enhance production methods, ensure model exploitability in constrained environments, and deliver robust tools that elevate both user experience and productivity. With a collaborative spirit at the heart of Safran.AI, you'll find a stimulating environment where creativity is celebrated and you can truly make your mark. If you have a strong background in AI deployment, a knack for developing user-centric solutions, and a collaborative approach to your work, we welcome you to help us transform industries and contribute to a safer world with your engineering prowess.

Frequently Asked Questions (FAQs) for MLOps Engineer Role at Safran.AI
What are the primary responsibilities of an MLOps Engineer at Safran.AI?

As an MLOps Engineer at Safran.AI, your main responsibilities will include enhancing the scalability and maintainability of our MLOps platform, collaborating with data scientists to improve the modularity of tools, and developing solutions for efficient model deployment. You’ll also be tasked with ensuring the effective performance of our models in resource-constrained environments and staying updated on technological advancements to refine our tech stack.

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What qualifications do I need to apply for the MLOps Engineer position at Safran.AI?

To apply for the MLOps Engineer position at Safran.AI, you should have at least 3 years of experience in AI production, excellent proficiency in Python, and a solid understanding of databases and containerization. Experience with model deployment, monitoring, and observability is also important. We value a user-centric approach and effective communication skills as key qualifications for this role.

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How does Safran.AI support the professional growth of MLOps Engineers?

At Safran.AI, we emphasize the importance of continuous learning and professional development. MLOps Engineers enjoy dedicated afternoons for personal projects and skill development. Additionally, there are tailored programs designed to enhance both professional and personal growth, ensuring you are always at the forefront of innovation in the AI domain.

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What type of work environment can I expect as an MLOps Engineer at Safran.AI?

As an MLOps Engineer at Safran.AI, you can expect a remote-friendly work environment where you can work up to three days a week from home. We foster a collaborative atmosphere focused on teamwork, creativity, and innovation, allowing you to engage actively with talented professionals while contributing to groundbreaking AI solutions.

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What are the typical career paths for MLOps Engineers at Safran.AI?

MLOps Engineers at Safran.AI often have the opportunity to advance within our AI Engineering team or transition into leadership roles in project management or technical direction. As the company grows, there will be numerous avenues to explore, whether in technical specializations or in shaping the strategic direction of AI initiatives.

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Common Interview Questions for MLOps Engineer
Can you explain your experience with deploying machine learning models?

When answering this question, emphasize specific projects where you've successfully deployed models. Discuss any tools or frameworks you used, such as Triton or Tensorflow Serving, and the challenges you faced, along with how you overcame them to ensure smooth deployment.

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How do you ensure the scalability of models in a production environment?

Highlight your experience with architectural patterns that support scalability. Discuss approaches like microservices or containerization, and how you monitor model performance to adapt or scale resources as needed, ensuring reliability and efficiency.

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What strategies do you employ to maintain model performance over time?

It's important to discuss routine model evaluations, re-training protocols, and data drift monitoring strategies. Share how you implement automated pipelines that keep models up-to-date and performing as expected in varying conditions.

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Describe a time when you improved an existing MLOps process.

Detail a specific example where you identified inefficiencies in an MLOps pipeline. Describe the actions you took to improve that process, the impact it had on productivity, and how it benefitted the team as a whole.

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How do you approach collaboration with data scientists?

Illustrate your collaborative approach by sharing experiences where clear communication led to a successful project outcome. Discuss any specific tools or practices you use to facilitate teamwork, ensuring that both engineering and data science perspectives are considered.

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What is your experience with cloud services in MLOps?

Talk about specific cloud providers you have worked with and how you leveraged services for model deployment, data storage, and processing power. Mention any particular challenges and how you optimized the use of cloud resources for MLOps.

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Can you discuss your experience with version control in AI development?

Emphasize the importance of version control in managing both code and models. Share your experience with tools like Git, and how you implemented practices for tracking changes and facilitating collaboration among team members.

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How do you prioritize user needs in your MLOps solutions?

Discuss your user-centric philosophy and provide an example of how you've gathered feedback from users to influence MLOps tool development. Highlight your commitment to delivering value that directly aligns with user requirements.

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What measures do you take to ensure high-quality code in your MLOps projects?

You can talk about coding standards, regular code reviews, automated testing practices, and continuous integration methods you use to maintain high-quality software. Emphasize your attention to detail and commitment to best practices in software development.

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How do you keep current with trends in MLOps and AI in general?

Share your strategies for staying informed about trends, such as following leading AI publications, attending workshops or webinars, and actively participating in relevant communities. This showcases your dedication to continuous learning and your proactive approach to professional development.

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Full-time, hybrid
DATE POSTED
November 30, 2024

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