Principal Engineer - AI Platform

Berkley•Wilmington, DE

About The Position

The AI Platform, Principal Engineer is responsible for designing, building, and maintaining robust AI platforms that support advanced machine learning and artificial intelligence applications. This role works closely with data scientists, software engineers, and product managers to develop scalable solutions that enhance the efficiency, reliability, and performance of our AI systems.

Requirements

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
Excellent 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
Bachelors Degree in Computer Science, Information Technology, Information Systems, or a related discipline.
Equivalent experience and/or alternative qualifications will be considered.
10%-20% of Travel Required.

Nice To Haves

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.

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
Stay current with the latest advancements in AI and machine learning technologies and incorporate them 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.