Netomi - SDE II
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Requirements
• 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. • 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.
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.
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