About the job
We are looking for a talented and passionate Principal Ai Engineer with a deep understanding of distributed systems, microservices architecture, and extensive experience in Python. This is an exciting opportunity for someone with a strong foundation in software engineering and a passion for building high-quality, data-intensive, and AI-powered systems. You will have the autonomy to lead complex projects while collaborating with cross-functional teams to deliver impactful solutions.
Responsabilities
Responsabilities
- Design, implement, and optimize scalable, high-performance distributed systems and microservices.
- Develop and maintain systems using Python and other relevant technologies.
- Utilize message brokers such as Kafka or RabbitMQ to design and implement messaging patterns.
- Work autonomously on highly complex tasks, ensuring performance, security, observability, and code quality.
- Lead and participate in the implementation of DevOps/MLOps practices, including containerization technologies (Docker, Kubernetes, etc.).
- Collaborate with cross-functional teams, providing technical insights and guidance to stakeholders.
- Monitor, troubleshoot, and improve production systems to ensure optimal performance and uptime.
- Make data-driven decisions to address system issues or enhance functionality.
- 5+ years of experience with Python in production environments.
- 10+ years of experience in software development, working with a diverse set of programming languages and paradigms.
- Proven track record of designing, implementing, and scaling performant distributed systems and microservice architectures.
- Deep understanding of classic Machine Learning, Deep Learning, and Generative AI algorithms.
- Familiarity with the ML development lifecycle and its differences from traditional software development workflows.
- Experience with message brokers such as Kafka or RabbitMQ and understanding of messaging patterns.
- Solid experience in DevOps/MLOps practices, including containerization technologies (e.g., Docker, Kubernetes).
- Familiarity with big data technologies such as Apache Spark, Delta Lake, Apache Kafka, Apache Flink, and NoSQL databases.
