Job description

MEET ELOQUENT AI At Eloquent AI, we’re building the next generation of AI Operators—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology goes far beyond chat: it sees, reads, clicks, types, and makes decisions—transforming how work gets done in regulated, high-stakes environments. We’re already powering some of the world’s leading financial institutions and insurers, fundamentally changing how millions of people manage their finances every day. From automating compliance reviews to handling customer operations, our Operators are quietly replacing repetitive, manual tasks with intelligent, end-to-end execution. Headquartered in San Francisco with a global footprint, Eloquent AI is a fast-growing company backed by top-tier investors. Join us to work alongside world-class talent in AI, engineering, and product as we redefine the future of financial services. Your Role As a Senior Software Engineer, AIOps & Infrastructure at Eloquent AI, 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. If you’re passionate about scalable AI systems and optimizing ML models for real-world applications, this is your opportunity to work at the frontier of LLMOps. You will: - 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. Bonus Points If… - You have experience with LLMOps, fine-tuning, and deploying large-scale AI models. - You’ve worked with GPU workload optimization, ML model parallelization, or distributed training strategies. - You have experience building infrastructure for AI-powered applications. - You’ve contributed to open-source MLOps tools or AI infrastructure projects. - You thrive in a fast-moving startup environment and enjoy solving complex technical challenges.

Requirements

Must-haves

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.
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.
Strong problem-solving skills and the ability to work in a high-scale, production-focused AI environment.
You have experience with LLMOps, fine-tuning, and deploying large-scale AI models.
You’ve worked with GPU workload optimization, ML model parallelization, or distributed training strategies.
You have experience building infrastructure for AI-powered applications.
You’ve contributed to open-source MLOps tools or AI infrastructure projects.
You thrive in a fast-moving startup environment and enjoy solving complex technical challenges.

Nice-to-haves

Familiarity with vector databases, retrieval systems, and RAG architectures is a plus.
Bonus Points If…

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningkubernetesllmmlawsgcpazurepython

Platform: ashby
Effort: quick