Principal Engineer - AI Platform

Responsibilities

• Design and develop scalable AI platforms to support various machine learning and deep learning models.
• Collaborate with data scientists to understand model requirements and optimize infrastructure accordingly.
• Implement and maintain data pipelines, model deployment strategies, and monitoring systems.
• Ensure high availability and reliability of AI services through effective troubleshooting and performance tuning.
• Develop and maintain systems primarily using Python, R, or Java, focusing on building REST APIs and small/secure web front ends.
• Design and implement database schemas and queries for relational, NoSQL and Graph databases.
• Integrate with third-party services, including Kafka, Entra ID, Collaborate with the frontend team to integrate frontend and backend systems seamlessly.
• Ensure the scalability, reliability, and security of backend systems.
• Troubleshoot and debug issues as they arise.
• Write optimized code and provide innovative solutions to complex problems.
• Stay updated with industry trends and emerging technologies.
• Incorporate the latest advancements in AI and machine learning technologies into the platform.
• Automate repetitive tasks and improve platform efficiency through scripting and configuration management tools.
• Provide technical guidance and support to other teams regarding AI platform usage and best practices.
• Document platform architecture, workflows, and procedures for future reference and scalability.

Qualifications

• 10+ years progressive engineering experience developing/engineering solutions and leading multiple large-scale data-domain or cross-domain engineering initiatives.
• Proven experience in building and maintaining AI or machine learning platforms.
• Strong knowledge of programming languages such as Python, R, and/or Java.
• Experience with cloud platforms (AWS, Azure) and containerization technologies (Docker, Kubernetes).
• Experience in optimizing code and Infrastructure for large data set manipulation.
• Excellent knowledge of MySQL, SQL Server, PostgreSQL, MongoDB, and/or CosmosDB.
• Excellent knowledge of modern Vector and Graph databases.
• Excellent knowledge of Redis cache or other similar solutions.
• Experience with version control systems like Git and GitLab and Agile development methodologies.
• Excellent understanding NLP, and AI/ML design and implementation concepts.
• Excellent understanding of REST APIs, API Gateways and SPAs.
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Strong communication and collaboration skills.
• Excellent knowledge of data storage solutions and database management systems.
• Proficiency in object-relational mapping (ORM), advanced algorithms, data structures, object-oriented and functional design principles, and best-practice patterns.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn.
• Multiple years working with structured and unstructured data within a cloud-based data environment (Redshift, Data Bricks, Hadoop, Snowflake, Spark, unmanaged Kafka, etc.).
• Experience with Master Data Management (MDM) systems and processes.
• Experience with Data Loss Prevention (DLP), encryption/redaction strategies, etc. for protecting PII/sensitive data.
• Good understanding of Processor architecture and ability to work with providers to map use cases to appropriate platforms.
• 10%-20% of Travel Required.
• Advanced degree in computer science, data science, AI, or related field.
• Experience with big data technologies (Hadoop, Spark).
• Experience with reinforcement learning, generative AI, or autonomous systems.
• Knowledge of MLOps practices and tools.
• Knowledge of insurance-specific AI applications.
• Hands-on experience working with AI/ML as a Data Scientist.
• Experience in deploying machine learning models in production environments.
• Understanding of distributed systems and microservices architecture.