Principal AI Engineer

Let's start with the role

As a Principal AI Engineer, you will play a critical role in designing and implementing cutting-edge, scalable, high-performance, real-time Data & AI products. The ideal candidate will be a tenured Backend Engineer that has a proven track record in building robust and efficient software solutions and combined with a passion for emerging technologies would like to continue doing so in the fascinating area of AI.

As a Principal AI Engineer your main responsibilities will be

• Architecture: Architect scalable, secure, reliable and high-performance backend systems and their corresponding data storages. 
• Innovation: Tech-lead the AI tribe in order to deliver state-of-the-art AI models in production.
• Engineering Excellence: Lead by example by demonstrating best engineering practices in code development, code reviews and all other phases of the software development lifecycle.
• Mentorship: Inspire and mentor fellow tech leaders and senior individual contributors, fostering technical and soft skill growth.
• Performance Optimization: Lead the charge in identifying and enhancing system performance of existing data pipelines, ETL products and data storages.
• Seamless Integration: Work closely with cross-functional teams and stakeholders to ensure smooth integration of the AI products in Kaizen’s overall architecture.
• Staying Ahead: Stay ahead of the tech curve, applying emerging technologies to enhance our capabilities and disrupt the industry. 
• Strategic Technical Roadmap: Define the company-wide AI technical roadmap unencumbered by the more time-sensitive business needs of the product roadmap.

What you’ll bring

• 10+ years of experience in software development, working with a diverse set of programming languages and paradigms. 5+ years of experience working with Python.
• Proven track record of designing, implementing, optimizing and scaling performant, distributed systems and microservice architectures.
• Experience with message brokers such as Kafka or RabbitMQ and messaging patterns
• Experience with DevOps/MLOps practices and containerization technologies
• Ability to work autonomously on highly complex tasks, keeping in mind various aspects such as performance, security, observability, and code quality
• Passion for monitoring and troubleshooting production systems and being able to make data-driven decisions
• Excellent communication and collaboration skills targeting a diverse audience from stakeholders to cross-functional technical teams, in order to provide technical guidance and insight

Nice to have:

• A deep understanding of classic ML, Deep Learning  and genAI algorithms.
• A deep understanding of the ML development  lifecycle and how that differs from the traditional software development one.
• Experience in building data-intensive and AI-empowered products.
• Experience in building experimentation, feature serving and model serving platforms.
• Familiarity with a wide range of big data technologies, such as Apache Spark, Delta Lake, Apache Kafka, Apache Flink and NoSQL databases.

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