Senior AI Infrastructure Engineer – LLM & Retrieval Systems

AI Platform EngineerJoin our innovative team as an AI Platform Engineer and be at the forefront of developing scalable, high-impact AI infrastructure solutions. In this pivotal role, you will focus on crafting production AI services, from Retrieval-Augmented Generation (RAG) systems to agentic workflows, empowering research and engineering teams across the firm. Your expertise will shape how AI capabilities are delivered and maintained, ensuring seamless, reliable, and efficient AI operations.What You’ll Do:Develop and maintain internal AI services and APIs, including RAG pipelines, document ingestion, embeddings, retrieval, and relevance optimizationManage vector database performance, scalability, and data freshness to ensure optimal retrieval qualityDesign and document user-friendly APIs supporting agentic workflowsIntegrate model serving endpoints into application services, monitoring latency, reliability, and retrieval accuracyBuild resilient systems with fallback mechanisms, version control, and testing frameworksImplement comprehensive monitoring, logging, and tracing across AI servicesOversee service lifecycle management, from deployment to deprecation, and participate in operational incident responseRequired Skills:4+ years of experience in software or platform engineering, with exposure to AI/ML or LLM-based applicationsStrong Kubernetes expertise, particularly in GPU contexts and containerized environmentsProficiency with cloud infrastructure, especially AWS, networking fundamentals, and IAMHands-on experience operating production RAG systems and retrieval infrastructureSolid Python skills for building production-grade APIs and servicesDeep understanding of LLM fundamentals, including prompting, token management, and output reliabilityExcellent communication skills and ability to collaborate across technical and non-technical teamsNice to Have Skills:Experience with agentic AI systems and workflow orchestrationFamiliarity with LLM evaluation frameworks and quality measurement toolsExposure to model serving platforms and inference optimization techniquesKnowledge of embedding models, retrieval performance trade-offs, and data engineering pipelinesRelevant AWS or Kubernetes certificationsPreferred Education & Experience:Bachelor’s or Master’s degree in Computer Science, Engineering, or related fieldsProven track record in developing and operating large-scale AI infrastructure in production environmentsAdditional requirements may include cloud certifications and a proactive approach to operational excellence.Ready to make a significant impact in the AI space? Apply now and become a driving force behind cutting-edge AI platform innovation!