Senior AI Infrastructure Engineer – LLM & Retrieval Systems

AI Platform Engineer

Join 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 optimization

• Manage vector database performance, scalability, and data freshness to ensure optimal retrieval quality

• Design and document user-friendly APIs supporting agentic workflows

• Integrate model serving endpoints into application services, monitoring latency, reliability, and retrieval accuracy

• Build resilient systems with fallback mechanisms, version control, and testing frameworks

• Implement comprehensive monitoring, logging, and tracing across AI services

• Oversee service lifecycle management, from deployment to deprecation, and participate in operational incident response

Required Skills:

• 4+ years of experience in software or platform engineering, with exposure to AI/ML or LLM-based applications

• Strong Kubernetes expertise, particularly in GPU contexts and containerized environments

• Proficiency with cloud infrastructure, especially AWS, networking fundamentals, and IAM

• Hands-on experience operating production RAG systems and retrieval infrastructure

• Solid Python skills for building production-grade APIs and services

• Deep understanding of LLM fundamentals, including prompting, token management, and output reliability

• Excellent communication skills and ability to collaborate across technical and non-technical teams

Nice to Have Skills:

• Experience with agentic AI systems and workflow orchestration

• Familiarity with LLM evaluation frameworks and quality measurement tools

• Exposure to model serving platforms and inference optimization techniques

• Knowledge of embedding models, retrieval performance trade-offs, and data engineering pipelines

• Relevant AWS or Kubernetes certifications

Preferred Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related fields

• Proven track record in developing and operating large-scale AI infrastructure in production environments

Additional 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!