AEROCONTACT
Website:
aerocontact.com
Job details:
Safran est un groupe international de haute technologie opérant dans les domaines de l'aéronautique (propulsion, équipements et intérieurs), de l'espace et de la défense. Sa mission : contribuer durablement à un monde plus sûr, où le transport aérien devient toujours plus respectueux de l'environnement, plus confortable et plus accessible. Implanté sur tous les continents, le Groupe emploie 100 000 collaborateurs pour un chiffre d'affaires de 27,3 milliards d'euros en 2024, et occupe, seul ou en partenariat, des positions de premier plan mondial ou européen sur ses marchés. Safran est la 2ème entreprise du secteur aéronautique et défense du classement « World's Best Companies 2024 » du magazine TIME. Safran Electrical & Power est l'un des leaders mondiaux des systèmes électriques aéronautiques. La société est un acteur clé dans le domaine de l'électrification des équipements et de la propulsion électrique et hybride. Elle compte 14 000 collaborateurs répartis dans 13 pays.
Mission description
Contribute to the development of microservices architecture, focusing on data ingestion, complex backend services, and high-velocity data applications. Design and implement scalable backend services using AWS cloud technologies. Develop, deploy, and optimize ChatGPT-based generative AI agents tailored for various applications including conversational interfaces, data analysis, and automation. Microservices Architecture: Architect and implement microservices with independently deployable modules to ensure improved fault isolation, granular scalability, and facilitated continuous delivery. Utilize Docker and Kubernetes for containerization and orchestration of microservices. Implement API gateways and service meshes (e.g., Istio) for managing internal communication, load balancing, and security policies of microservices. Data-Driven Solutions: Build sophisticated data analytics platforms capable of processing and analyzing large datasets in real-time. Utilize streaming technologies like Apache Kafka or AWS Kinesis for real-time data ingestion and processing. Implement ETL pipelines using AWS Glue, AWS Lambda, or similar technologies. Collaborate: Work closely with cross-functional teams, including frontend developers, data scientists, and DevOps engineers, to deliver comprehensive solutions. REST/GraphQL Services: Design and implement robust APIs using REST and GraphQL standards. Implement GraphQL servers using Apollo or similar frameworks. Utilize RESTful API principles for efficient and scalable endpoint design. Database Management: Work with SQL and NoSQL databases to ensure efficient and secure data storage, retrieval, and analysis. Design and optimize database schemas for performance and scalability. Implement data caching strategies using Redis or Memcached. Design Patterns and Best Practices: Uphold and advocate for software design patterns and coding standards to maintain high-quality codebases. Implement patterns such as Singleton, Factory, Observer, Strategy, and Circuit Breaker to ensure code robustness and maintainability.
Experience: Proven track record as a Full Stack Engineer with significant experience in backend development using AWS, NodeJS, and Python. Technical Skills: AWS Services: EC2, Lambda, S3, CloudFormation, ECS, RDS, DynamoDB, and other core AWS services. Programming Languages: Proficient in NodeJS, Python, and Django. Microservices Architecture: Experience in designing and implementing microservices. API Development: Strong understanding of REST and GraphQL APIs. Database Proficiency: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases. Design Patterns: Familiarity with software design patterns and best practices. Additional Competencies: IoT Integration: Experience with IoT platforms, data ingestion techniques, and communication protocols (e.g., MQTT, AMQP). GenAI: Experience with developing AI models, specifically ChatGPT or similar technologies. Familiarity with OpenAI API and fine-tuning GPT models. Experience in deploying and scaling AI models in production environments. Data Analytics: Strong background in building and optimizing data analytics platforms. Proficient with big data technologies (e.g., Hadoop, Spark) and BI tools (e.g., Tableau, PowerBI). Collaboration: Strong communication skills and experience working in agile environments. Adaptability: Ability to quickly learn new technologies and thrive in a fast-paced, dynamic environment.
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