AI Systems Engineer

AI Systems EngineerLocation: Bay Area, CaliforniaDepartment: Engineering / Applied AIExperience: 5+ yearsCompany OverviewWe are an AI technology company building production-grade intelligent systems for complex, data-intensive industries. Our platform combines generative AI, machine learning, retrieval, and automation to help enterprise teams turn operational goals into actions.Role OverviewWe are looking for an experienced AI Systems Engineer to build and deploy the machine learning and generative AI systems at the core of our platform.You will own projects from prototype through production, working with real-world operational data and business-critical workflows. Experience in telecommunications, networking, or infrastructure is a strong advantage.Key ResponsibilitiesTranslate product and domain requirements into technical AI and machine learning solutions.Design and deliver end-to-end systems from experimentation to production.Develop ML, deep learning, and reinforcement learning models.Improve retrieval and grounding systems using proprietary enterprise data.Build LLM agents, tool integrations, and automated workflows.Establish evaluation, monitoring, and observability practices.Mentor engineers and help shape the technical direction of the AI team.QualificationsMaster’s or Ph.D. in Computer Science, Electrical Engineering, or a related field.5+ years of experience building production AI or machine learning systems.Strong ownership of the full ML lifecycle, including data preparation, training, evaluation, deployment, and monitoring.Experience with semantic search, embeddings, vector and hybrid retrieval, reranking, and retrieval evaluation.Experience building production LLM agents involving planning, memory, tool use, orchestration, guardrails, and observability.Ability to work independently with complex data models and schemas.Familiarity with MLOps, CI/CD, model versioning, containerization, and cloud deployment.Preferred ExperienceTelecommunications, networking, infrastructure, or other operationally complex domains.Reinforcement learning, optimization, or decision-making systems.Fast-paced startup or product engineering environments.