AI Engineer, AIOps & Infrastructure
Eloquentai · San Francisco
About this role
## About Eloquent AI Eloquent AI is building the next generation of AI Operators—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology goes beyond chat: it can see, read, click, type, and make decisions, helping automate work in regulated, high-stakes environments. We already power leading financial institutions and insurers, replacing repetitive manual tasks with intelligent, end-to-end execution. Headquartered in San Francisco with a global footprint, we’re backed by top-tier investors. ## Role: Senior Software Engineer, AIOps & Infrastructure As a Senior Software Engineer (AIOps & Infrastructure), you’ll design, build, and optimize scalable, high-performance AI infrastructure to support the deployment and operation of enterprise AI agents. You’ll enable ML engineers and AI teams to train, fine-tune, and deploy LLMs efficiently—while ensuring stability, observability, and performance at scale. You’ll also help automate LLMOps and MLOps workflows, optimize GPU workloads, and ensure resilient, production-ready AI systems. ### You will: - Design and build scalable ML infrastructure for deploying and maintaining AI agents in production. - Automate LLMOps and MLOps workflows (training, fine-tuning, deployment, and monitoring). - Optimize GPU and cloud compute workloads to improve efficiency and reduce latency. - Develop Kubernetes-based solutions, including custom operators for ML model orchestration. - Improve observability and reliability with logging, monitoring, and performance tracking. - Partner with ML and engineering teams to streamline data pipelines, model serving, and inference optimizations. - Ensure security, compliance, and reliability in AI infrastructure (high availability and scalability). - Participate in on-call rotations to maintain 24/7 reliability. ## Requirements - 5+ years of experience in software engineering, MLOps, or infrastructure development. - Strong expertise in Kubernetes and experience managing containerized ML workloads. - Deep understanding of cloud platforms (AWS, GCP, Azure) and distributed computing. - Proficiency in Python, including building services for ML/AI applications. - Experience with ML model deployment pipelines (model serving, inference optimization, monitoring). - Familiarity with vector databases, retrieval systems, and RAG architectures (plus). - Strong problem-solving skills in a high-scale, production-focused AI environment. ## Bonus Points (If you have…) - Experience with LLMOps, fine-tuning, and deploying large-scale AI models. - GPU workload optimization, ML model parallelization, or distributed training strategies. - Experience building infrastructure for AI-powered applications. - Contributions to open-source MLOps tools or AI infrastructure projects. - Enjoyment of fast-moving startup environments and complex technical challenges.
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