About the role
Responsibilities:
- Define, own, and continuously evolve the end-to-end architecture for o9's enterprise platform, ensuring solutions meet enterprise standards for scalability, reliability, security, performance, and cost efficiency when deploying technically advanced products with a significant amount of deep learning models embedded in them.
- Architect and guide the implementation of scalable, secure data pipelines using modern AI-boosted data engineering technologies, enabling efficient ingestion, processing, and serving of large volumes of structured and unstructured data.
- Lead the productionalization of AI systems by defining and governing MLOps best practices, including model registries, automated training and retraining pipelines, CI/CD for ML, deployment strategies, and continuous monitoring to detect model drift, data drift, and performance degradation.
- Design, train, fine-tune, and deploy deep learning as well as Agentic models, implementing advanced architectures such as Retrieval-Augmented Generation (RAG) and integrating vector database technologies to enable scalable, low-latency, and context-aware AI applications.
- Serve as a principal-level technical leader, mentoring senior and staff engineers, leading architecture and design reviews, and setting engineering standards and patterns that ensure consistency and quality across teams.
- Translate complex business, product, and operational requirements into clear, durable technical architectures, balancing short-term delivery needs with long-term platform evolution.
Requirements:
- Experience: 10+ years of experience in software architecture and building large-scale, production-grade systems, with 5+ years of specialization in deep learning and data engineering, supported by strong computer science fundamentals.
- Deep Learning and AI Expertise: Strong proficiency in modern deep learning frameworks and agentic AI systems, with hands-on experience across model development, fine-tuning, deployment, and lifecycle management.
- Data Technologies: Proven experience handling unstructured and semi-structured data and working with modern data engineering technologies to support large-scale, distributed AI workloads.
- Programming and System Design: Mastery of Python, solid backend engineering experience, and deep expertise in algorithms, data structures, distributed systems, and large-scale system design. Additional experience with other languages such as Scala, Kotlin, and Rust is highly desirable.
- Big Data and Distributed Computing: Demonstrated experience processing large datasets and building solutions using distributed computing frameworks in production environments.
- Architectural Leadership: Ability to influence architectural decisions across teams, drive technical consensus, and make trade-off decisions that shape long-term platform direction.