AI Engineer, AIOps & Infrastructure
Remote • San Francisco • FullTime
Posted 1y ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
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 transforms how work gets done in regulated, high-stakes environments, powering leading financial institutions and insurers. We are a fast-growing company backed by top-tier investors, seeking to redefine the future of financial services.
As a Senior Software Engineer, AIOps & Infrastructure, you will be responsible for designing, building, and optimizing scalable, high-performance AI infrastructure to support the deployment and operation of our enterprise AI agents. Your work will enable machine learning engineers and AI teams to train, fine-tune, and deploy LLMs efficiently while ensuring stability, observability, and performance at scale. You’ll play a key role in automating LLMOps and MLOps workflows, optimizing GPU workloads, and ensuring resilient, production-ready AI systems. This role requires deep expertise in cloud infrastructure, Kubernetes, and LLM and ML deployment pipelines.
Responsibilities
- Design and build scalable ML infrastructure for deploying and maintaining AI agents in production.
- Automate LLMOps and MLOps workflows, ensuring seamless model training, fine-tuning, deployment, and monitoring.
- Optimize GPU and cloud compute workloads, improving efficiency and reducing latency for large-scale AI systems.
- Develop Kubernetes-based solutions, including custom operators for ML model orchestration.
- Improve system observability and reliability, implementing logging, monitoring, and performance tracking for AI models.
- Work with ML and engineering teams to streamline data pipelines, model serving, and inference optimizations.
- Ensure security, compliance, and reliability in AI infrastructure, maintaining high availability and scalability.
- Participate in on-call rotations, ensuring 24/7 reliability of critical AI systems.
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, with experience developing services for ML/AI applications.
- Experience with ML model deployment pipelines, including model serving, inference optimization, and monitoring.
- Familiarity with vector databases, retrieval systems, and RAG architectures is a plus.
- Strong problem-solving skills and the ability to work in a high-scale, production-focused AI environment.