About the Company:
Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, MGM, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences.
Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us!
Do you believe in the missions of intelligence agencies? Are you interested in building state-of-the-art NLP models and solving complex technical challenges? Do you want to be a part of our journey in shaping the future of Automated Customer Service?
If you are interested in working on some of the most challenging technical and programmatic issues, we would love to discuss with you about the exciting work and career opportunities at Netomi .
Job Responsibilities
- Design, develop, and deploy scalable, high-performance software systems and infrastructure to solve complex business problems.
- Collaborate closely with Data Scientists to support the integration of machine learning models and data pipelines into production systems, focusing primarily on software engineering aspects (e.g., code optimization, deployment, and system integration).
- Work with Product & Engineering teams to integrate solutions into products and services, emphasizing system design, coding, and best practices.
- Develop and manage data pipelines to handle large datasets, ensuring efficient data ingestion, transformation, and storage to support both data scientists and engineering needs.
- Architect and implement scalable software systems, optimize existing systems, and build new features with a focus on system efficiency and scalability.
- Manage databases and caching systems (e.g., MySQL, Redis, Elasticsearch), ensuring efficient data storage and retrieval, while supporting the needs of data scientists working with large datasets.
- Deploy robust infrastructure solutions on AWS or GCP, ensuring high availability, fault tolerance, and scalability for data-heavy applications.
- Conduct experiments and test hypotheses to improve system performance and reliability, leveraging collaboration with data scientists to ensure models and algorithms are properly integrated into production.
- Communicate technical details and insights to both engineering and non-engineering stakeholders, fostering cross-functional understanding.
- Stay updated on the latest developments in software engineering, system design, and infrastructure best practices, sharing knowledge with the broader team.
<