Software Development Engineer, Technical Leader

Requirements

• Requires a Bachelor's degree in Computer Science, Computer Engineering, a related field, or a foreign degree equivalent. Must have 5 years of experience in job offered or related occupation. Must have 5 years of experience in:
• 5 years of experience utilizing foundational Java and basic frameworks
• 5 years of experience MySQL database design and performance tuning using MySQL Workbench and dibdiagram.io for team collaboration.
• 5 years of experience utilizing NoSQL databases including DynamoDB and MongoDB in use cases and design patterns; utilizing middleware technologies including Memcached, Redis, Kafka, RocktMQ, and Elasticsearch;
• 5 years of experience utilizing design patterns including Factory, Builder, Strategy, Repository, and Observer;
• 5 years of experience utilizing Linux, Nginx, and Tomcat in development and tuning; and working with large-scale distributed SaaS systems and CaaS systems.
• Must have 3 years of experience in:
• 3 years of experience using Machine Learning (ML) theories including Natural Language Processing (NLP) and Support Vector Machine (SVM) for text processing, sentiment analysis, classification, and summarization.
• 3 years of experience using Machine Learning (ML) frameworks including TensorFlow for production model training and deployment and PyTorch for research and fast prototyping of NLP or computer vision models.
• 3 years of experience in utilizing RAG (Retrieval-Augmented Generation) AI integrations to support query skill in AI
• 3 years of experience working with DB data migration and workflow services development; and Python programming language.
• Telecommuting work arrangement permitted: position may work in various unanticipated locations throughout the U.S.
• Zoom Communications, Inc.
• #LI-DNI

Benefits

Additional Information

Immigration sponsorship is not available for this position Responsibilities: Lead a high-performing software engineering team to architect, develop, and deliver AI-driven, scalable enterprise solutions, including cloud-based file management platforms, real-time collaboration tools, and proactive monitoring systems supporting 99.99% uptime. Utilize advanced technologies such as retrieval-augmented generation (RAG), large language models (LLMs), domain-optimized AI models, and workflow orchestration to drive product innovation, optimize distributed architecture, and mentor engineers in designing resilient, high-performance systems serving millions of global users. Provide technical leadership for designing, implementing, and optimizing AI-powered features, including RAG-based knowledge retrieval and AI-driven conversational agents, ensuring alignment with enterprise objectives. Mentor engineers in advanced software engineering practices, AI model integration, and system performance optimization for large-scale, cloud-native SaaS platforms. Collaborate with cross-functional teams to resolve complex technical challenges, evaluate trade-offs, and ensure efficient resource utilization. Architect and evolve cloud-based file management platforms capable of processing over 30 TB of data per day, with distributed storage, synchronization, AI-assisted metadata extraction (including OCR), search, and classification capabilities. Lead the design and implementation of low-latency real-time chat and collaboration systems supporting millions of concurrent users. Develop workflow automation systems incorporating rule-based processing, AI-assisted orchestration, and intelligent agentic workflows for diverse business scenarios. Integrate AI agents with LLMs and small language models to enable intelligent querying, recommendations, and smart compose features. Design scalable, maintainable, and highly available architectures for distributed systems deployed on public or hybrid cloud environments (e.g., AWS, Azure) based on Kubernetes. Optimize system performance to handle over 2000 requests per second, sustaining throughput exceeding 100 million requests per day, while maintaining p99 latency under 1000 ms for core APIs under peak load conditions. Implement CI/CD pipelines, automated testing frameworks, and performance tuning processes to ensure system reliability and rapid iteration cycles. Develop and configure enterprise-grade monitoring and alerting solutions (e.g., Prometheus, Grafana, PagerDuty) for proactive incident detection and resolution. Implement disaster recovery strategies reducing recovery time objectives to under 5 minutes, recovery point objectives to under 1 minute and achieving operational continuity with 99.99% uptime. Analyze system metrics to identify bottlenecks, applying preventive measures to maintain service quality under peak loads.

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