Job Description

At Netomi AI, we are on a mission to create artificial intelligence that builds customer love for the world’s largest global brands.

Some of the largest brands are already using Netomi AI’s platform to solve mission-critical problems. This would allow you to work with top-tier clients at the senior level and build your network.

Backed by the world’s leading investors such as Y-Combinator, Index Ventures, Jeffrey Katzenberg (co-founder of DreamWorks) and Greg Brockman (co-founder & President of OpenAI/ChatGPT), you will become a part of an elite group of visionaries who are defining the future of AI for customer experience. We are building a dynamic, fast growing team that values innovation, creativity, and hard work. You will have the chance to significantly impact the company’s success while developing your skills and career in AI.

Want to become a key part of the Generative AI revolution? We should talk.

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.

As a Lead Data Scientist at Netomi, you will drive NLP and machine learning projects and be responsible for developing methodology and solutions to support technical, analytical, and operational requirements.

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.
  • Provide technical mentorship and guidance to junior team members, encouraging continuous learning and development.
  • Ensure system compliance with data security and privacy regulations, incorporating best practices into development processes.

Requirements

• • 3+ years of experience as a software engineer, with a focus on system design, coding, and best practices, preferably in a product development environment.
  • Strong programming skills in Python or other relevant programming languages, with experience in building scalable systems.
  • Experience collaborating with Data Scientists or working on projects involving data pipelines, with an understanding of how to support machine learning model deployment and data-driven systems from a software perspective.
  • Proficiency in using key components of the tech stack including Elasticsearch, Redis, MySQL, and AWS services (e.g., EC2, RDS, S3, Lambda), with a deep understanding of system design principles.
  • Excellent communication skills, with the ability to explain complex concepts to technical and non-technical stakeholders.
  • Strong problem-solving and analytical skills, with a keen interest in continuous learning and skill development.
  • Experience optimizing infrastructure and software deployment to reduce latency and improve cost efficiency.
  • Optional: Experience with modern machine learning and data processing technologies such as Apache Airflow, Kubernetes, MLflow, and Hugging Face for managing machine learning workflows and distributed systems.