Senior Software Engineer - Agentic Systems and Platform

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards.
About the Role: We are seeking a highly skilled Software Engineer with deep expertise in designing, building, and scaling sophisticated AI systems for the enterprise. In this senior role, you will lead the architectural design and hands-on implementation of our next-generation agentic platform and core enterprise AI capabilities. This involves taking ownership of critical system components from conception to production, pioneering innovative agentic architectures, developing advanced search and retrieval systems, and designing robust APIs for seamless enterprise integration. You will be instrumental in translating cutting-edge research into reliable, scalable, and secure production systems, mentoring other engineers, and contributing significantly to our technical strategy and best practices.

Responsibilities

• Lead the architectural design and hands-on development of core components for our enterprise AI platform, focusing on agentic systems, search, and integration layers.
• Design, build, and optimize sophisticated agentic workflows and multi-agent systems (e.g., DeepResearch, voice agent), exploring and implementing coordination mechanisms and protocols.
• Architect and implement highly scalable and performant modern search and retrieval systems (e.g., advanced RAG, knowledge graphs, DeepSearch) capable of operating over massive, complex enterprise data.
• Design and develop robust, secure, and well-documented APIs for interacting with AI models and agentic systems, ensuring ease of integration into diverse enterprise environments.
• Establish and enforce best practices for system design, coding standards, testing, deployment, and monitoring to ensure the highest levels of quality, reliability, security, and scalability in production.
• Collaborate closely with AI researchers to translate novel research findings into practical, production-ready system designs and implementations.
• Contribute to the technical roadmap and strategic planning for the AI platform.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field, or equivalent practical experience with a strong track record of delivering complex systems.
• Strong contribution record on GitHub. Please include your GitHub link in your application.
• 3+ years of experience designing, building, and deploying production-grade machine learning systems at scale.
• Proven track record of architecting and implementing complex software systems from scratch, with a strong understanding of distributed systems and microservices.
• Extensive experience designing and implementing robust APIs for technical products.
• Demonstrated ability to lead technical initiatives and mentor other engineers.
• Expert-level programming skills (Python, Rust, Go, Java, C++, etc.) and deep experience with relevant ML frameworks (e.g., PyTorch, JAX, TensorFlow).
• Exceptional problem-solving, system design, and architectural skills.
• Unwavering commitment to quality, reliability, security, and performance in enterprise software development.

Bonus point

• Solid understanding of HTTP protocol and real-time communication protocols (e.g., WebRTC) for voice AI. 
• Experience with large language or multimodal models, and advanced techniques like fine-tuning, RAG, and agentic design.
• Significant experience with major cloud platforms (AWS, GCP, Azure) and advanced MLOps practices at scale.
• Experience with distributed training, inference optimization, and serving large models efficiently.
• Familiarity with enterprise architecture patterns, data governance, and security compliance.