Principal Software Engineer - Backend AI Engineer

Job Summary The Principal Software Engineer is a high-level individual contributor and technical leader responsible for the architecture, stability, and evolution of core backend services. You will exercise expert judgment to design scalable distributed systems, high-performance APIs, and robust data models. While your foundation is in core backend engineering, you will also lead the integration of LLMs and AI Agentic workflows to solve complex automation challenges and enhance product intelligence. You will provide technical steering for the team, mentoring engineers while ensuring all solutions align with Red Hat’s standards for security, privacy, performance and the ability to handle sensitive customer data with the highest level of integrity. You will act as the technical backbone of our engineering efforts, balancing the tried and true stability of robust backend engineering with the cutting edge potential of AI Agents. This is a role for a builder who loves deep system design as much as they enjoy experimenting with the future of automation. At Red Hat, our commitment to open source innovation extends beyond our products - it’s embedded in how we work and grow. Red Hatters embrace change – especially in our fast-moving technological landscape – and have a strong growth mindset. That's why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers' most pressing challenges. What you will do System Architecture: Lead the design and implementation of highly scalable, resilient backend services using Python , ensuring best practices in microservices and distributed systems. API Leadership: Drive the strategy for API development, designing and maintaining high-performance RESTful and GraphQL endpoints that support complex frontend and internal requirements. Data Modeling & Strategy: Architect and optimize database schemas (SQL and NoSQL), ensuring data integrity, performance tuning, and efficient query design for high-traffic applications. Pragmatic AI Integration: Design and deploy AI-driven features, focusing on LLM orchestration and Agentic workflows (e.g., automated reasoning, tool-calling) to solve real-world engineering problems. Performance Engineering: Lead deep-dive debugging sessions and performance audits across the stack, from the database layer to the application logic. AI-Enhanced SDLC: Proactively utilize and experiment with AI-assisted tools (code generation, intelligent suggestions) to accelerate development cycles and improve code quality across the team. Cross-Functional Collaboration: Partner with Product Managers to translate Requests for Enhancement (RFEs) into technical specifications and lead the implementation of complex, multi-sprint features. What you will bring Mastery of Python and its ecosystem (e.g., FastAPI, Django, Flask), with a focus on writing clean, maintainable, and asynchronous code Deep expertise in building and consuming REST and GraphQL APIs; experience with API versioning, security (OAuth/JWT), and documentation Advanced knowledge of relational databases (e.g., PostgreSQL ) and NoSQL solutions (e.g., Redis, MongoDB), including indexing, migration strategies, and optimization Comfortable with cloud-native technologies (Kubernetes, OpenShift, or AWS/Azure) Practical experience integrating Large Language Models and building Retrieval-Augmented Generation (RAG) pipelines Familiarity with creating AI Agents that can perform tasks, use tools, and maintain state within a software ecosystem A foundational understanding of Machine Learning principles to effectively collaborate with data science teams Mastery of Git and advanced branching/merging strategies Proven ability to lead in an Agile environment, providing technical direction during sprint planning and code r ... (truncated, view full listing at source)