Service Development & Architecture: Design, implement, and deploy microservices and APIs using Python, focusing on scalability, resilience, and maintainability.
API Integration: Develop and maintain robust RESTful APIs and asynchronous event-driven services (e.g., using Kafka or RabbitMQ) for internal and external consumption.
Data Engineering & AI Pipelines: Contribute to the design and implementation of data ingestion, transformation, and processing pipelines. Integrate machine learning models and LLM-based functionalities into backend services.
Database Management: Work with relational (e.g., PostgreSQL) and/or NoSQL databases, including schema design, query optimization, and performance tuning. Experience with vector databases for AI applications is a strong plus.
Infrastructure & Deployment: Collaborate on CI/CD pipelines (e.g., Jenkins, GitLab CI) and container orchestration (e.g., Docker, Kubernetes). Support cloud infrastructure management (AWS).
Code Quality & Practices: Promote sound practices in coding, testing (unit, integration, end-to-end), and deployment. Conduct thorough code reviews and mentor junior engineers.
System Observability: Implement monitoring, logging, and alerting solutions to ensure system health and performance.
Security: Adhere to security practices in API design, data handling, and infrastructure deployment.
4/5+ years of professional experience in backend software development.
Python Expertise: Deep proficiency in Python, with experience using relevant frameworks (e.g., Flask, FastAPI, Django).
API Design & Development: Proven experience designing and implementing complex RESTful APIs, with a solid understanding of API versioning strategies. Familiarity with GraphQL is a plus.
Database Technologies: Strong experience with relational databases (e.g., PostgreSQL) and/or NoSQL databases (e.g., MongoDB, DynamoDB).
Cloud Computing: Hands-on experience with at least one major cloud provider (AWS preferred, given our current infrastructure). Understanding of serverless architectures and managed services.
Version Control: Expert-level proficiency with Git and collaborative development workflows (e.g., GitHub, GitLab).
Software Design Principles: Solid grasp of SOLID principles, design patterns, and microservices architecture.
Testing Frameworks: Experience with Python testing libraries (e.g., pytest, unittest).
Communication: Excellent written and verbal communication skills in English, with the ability to articulate technical concepts clearly.- Nice to have:
- Experience with Go or Node.js for specific microservices.
- Familiarity with message queues (e.g., Kafka, RabbitMQ, SQS).
- Exposure to container orchestration technologies, including Kubernetes.
- Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) and LLM integration (e.g., LangChain, OpenAI API).
- Knowledge of vector databases (e.g., Pinecone, Weaviate, Qdrant) and their application in search and recommendation systems.
- Experience with CI/CD tools and practices (e.g., Docker, Jenkins, GitLab CI).
Piattaforma all'avanguardia nell'Intelligenza Personale ed Enterprise basata su AI. Progettano sistemi che unificano fonti di dati e flussi di lavoro eterogenei tra le principali suite di produttività (Microsoft 365, Google Workspace) e le applicazioni aziendali (Slack, Salesforce). La loro missione è aumentare la produttività organizzativa e individuale attraverso l'automazione intelligente e l'AI contestuale.
Ottima possibilità di crescita
Azienda che utilizza tecnologie innovative