Rust Jobs for Rustaceans

**Key Responsibilities: **

• Design and develop generalized AI solutions, including RAG systems, to enhance user capabilities with our AI accelerators.
• Build high-performance, scalable applications using Rust for systems-level efficiency and Python for rapid prototyping and integration.
• Integrate and optimize large language models (LLMs) and neural network architectures into production-ready software.
• Collaborate with cross-functional teams to define requirements and implement AI-driven features.
• Work with databases (e.g., SQL, NoSQL) to manage and retrieve data efficiently for AI applications like RAG.
• Stay informed on the latest trends in AI, LLMs, neural networks, and related technologies to drive innovation.
• Write clean, maintainable, and well-documented code adhering to best practices.
• Troubleshoot and optimize AI applications for performance, reliability, and scalability.
• Contribute to the creation of reusable libraries and frameworks for AI development.

What We’re Looking For

• Experience building AI applications, with a focus on practical, production-grade solutions.
• Strong proficiency in Rust for performance-critical systems and Python for AI development and scripting.
• Solid understanding of large language models (LLMs), their mechanics, and their real-world applications.
• Familiarity with neural network architectures and how they power modern AI systems.
• Experience with conventional databases and vector databases (e.g., PostgreSQL, MongoDB, OpenSearch, Qdrant).
• Good grasp of Retrieval-Augmented Generation (RAG) and latest AI Agent frameworks.
• Ability to stay current with the fast-evolving AI landscape, including advancements in LLMs and neural networks.
• Strong problem-solving skills and a creative approach to building generalized solutions.
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).

Nice-to-Haves

• Experience with AI frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
• Familiarity with hardware-accelerated AI (e.g., GPUs, TPUs, or custom accelerators, FPGAs).
• Prior work with APIs or microservices for AI integration.