Parasail is a cloud computing provider that supplies specialized inference infrastructure for companies running generative AI models.
Parasail is redefining AI infrastructure by enabling seamless deployment across a distributed network of GPUs, optimizing for cost, performance, and flexibility. Our mission is to empower AI developers with a fast, cost-efficient, and scalable cloud experience—free from vendor lock-in and designed for the next generation of AI workloads. The Senior Software Engineer, Distributed Systems role at Parasail is crucial for ensuring the seamless deployment, scalability, and security of our AI platform. This position is tailored for individuals passionate about creating robust infrastructures that support complex AI workloads, with a strong emphasis on cloud compatibility, security, ease of use, and cost savings. You will work closely with AI-focused performance and solutions engineering teams, facilitating the orchestration of resources needed for high-performing AI applications, all while ensuring our infrastructure adheres to the highest standards of security and data privacy. TECHNICAL ABILITIES - Microservice Architecture: Deep understanding of distributed application design patterns, service decomposition, and inter-service communication. Experience in building scalable and maintainable microservice-based systems. - Backend Development: Strong foundation in storage solutions and networking protocols. Ability to design and implement robust backend systems with optimized data management and network communication. - Programming Languages & Frameworks: Extensive experience with Java, Spring Boot, and Golang. Proficiency in developing enterprise-grade applications using modern development frameworks and tools. - Distributed Systems: Deep knowledge of distributed computing principles, including consistency models, fault tolerance, and scalability patterns. Experience in designing and maintaining large-scale distributed applications. - Cloud-Native Technologies: Proficiency in cloud platforms and Infrastructure as Code (IaC). Experience with containerization, orchestration, and automated infrastructure deployment using modern DevOps practices. - Large Language Models: Understanding of LLM technologies and their applications. Experience in integrating and working with AI language models in production environments, including model deployment and optimization. QUALIFICATIONS - 5+ years of experience in backend or infrastructure, with exposure in Kubernetes and distributed computing environments. - Demonstrated ability to build and secure scalable, high-performance backend. - Hands-on to build brand new system and make end to end work - Excellent problem-solving skills, with a track record of designing solutions that address the complex needs of AI workloads. - While AI experience is not required, a demonstrated capacity to learn new technologies and stay up-to-date on the cutting edge is essential. WHAT YOU BRING TO THE TABLE This role is pivotal in building a new breed of cloud architecture that enables enterprises to build powerful AI in their preferred environment with the same performance and economics as uncontrolled external services. This role ensures our AI solutions are not just innovative but also secure and sustainable. If you're passionate about learning the cutting edge of AI, building the future of infrastructure, and making a tangible impact to the early days of a company, we're excited to welcome you aboard.