Principal Engineer, Software
Skills
About the Role
You will be the foundational pillar of the Content portfolio strategy, injecting deep expertise into the product portfolio. You'll research, architect, and pioneer solutions that process and extract intelligent insights from millions of unstructured documents and multi-media files every day. As a founding engineer for content, you'll be one of the primary authorities responsible for identifying and implementing state-of-the-art techniques for document ingestion, classification, entity extraction, image search, and more. You'll act as the chief technical authority, researching, evaluating, and prototyping cutting-edge solutions using LLMs, Computer Vision, and other techniques to solve complex data extraction challenges. You'll design and build robust, scalable, cost-effective AI services and data processing pipelines that serve as the backbone for processing millions of documents daily. You'll tackle real-world production AI challenges, including managing LLM latency and variance, developing prompt engineering strategies, and building fault-tolerant systems. You'll also serve as a key technical mentor and thought leader for the engineering team, driving mission-critical initiatives to production.
Requirements
- 14+ years of professional software engineering experience, with a proven track record of building complex, data-intensive, backend systems
- Deep expertise (8+ years) in building and scaling production-grade services using modern backend frameworks such as FastAPI, Django, Spring Boot or similar
- Mastery in designing large-scale distributed systems, demonstrating strong knowledge of asynchronous patterns, streaming/queuing/caching strategies, and robust observability (logging, metrics, tracing)
- Exceptional communication and leadership skills, with the ability to articulate complex technical concepts to diverse audiences and influence engineering direction across multiple teams without direct authority
- Hands-on experience in the complete lifecycle of AI/ML models: from experimentation and prototyping to deploying, monitoring, and iterating on them in a high-volume cloud environment
- Proficiency with modern DevOps and MLOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks
- Hands-on experience with containerization and orchestration technologies, particularly Docker and Kubernetes
Responsibilities
- Research, evaluate, and prototype cutting-edge solutions using Large Language Models, Computer Vision, and other techniques to solve complex data extraction challenges
- Design and build robust, highly scalable, and cost-effective AI services and data processing pipelines
- Manage LLM latency and variance and develop sophisticated prompt engineering strategies
- Build fault-tolerant, defensive systems that perform consistently
- Mentor engineers and act as a thought leader for the engineering team
- Drive mission-critical initiatives to production