PrincipalAIEngineer

## Accountabilities Define and lead the AI system architecture and technical strategy across the full lifecycle, from design through production deployment. Design and build scalable ML platforms, pipelines, and event-driven systems supporting distributed and asynchronous workloads. Architect and implement LLM-based systems including RAG pipelines, embeddings, vector databases, prompt engineering, and multi-agent orchestration. Develop and maintain backend services and APIs that integrate AI capabilities into enterprise and third-party systems. Lead model development, optimization, and productionization of AI solutions, ensuring reliability and scalability in real-world environments. Establish engineering standards and best practices for AI development, MLOps, monitoring, and system observability. Ensure system performance, security, and governance across all deployed AI solutions. Collaborate with product, engineering, and leadership teams to align AI initiatives with business priorities and roadmap execution. Mentor engineers and influence technical culture across teams, raising the overall bar for AI engineering excellence. Requirements: 7+ years of experience in AI/ML engineering with strong production-level experience. Advanced proficiency in Python and strong software engineering fundamentals, including system and API design. Deep experience with distributed systems, event-driven architectures, and cloud-native engineering patterns. Strong hands-on expertise with LLM systems including prompt engineering, tool/function calling, RAG architectures, embeddings, vector databases, and multi-agent systems. Proven experience building and operating MLOps pipelines, including deployment, monitoring, versioning, and reproducibility. Strong experience with AWS, including GenAI services such as AWS Bedrock, and familiarity with other cloud platforms. Experience working with both SQL and NoSQL databases and designing scalable data architectures. Familiarity with containeriza