AI Engineer, AIOps & Infrastructure

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.

Nice-to-Haves

• Experience with LLMOps, fine-tuning, and deploying large-scale AI models.
• Worked with GPU workload optimization, ML model parallelization, or distributed training strategies.
• Experience building infrastructure for AI-powered applications.
• Contributed to open-source MLOps tools or AI infrastructure projects.
• Thrive in a fast-moving startup environment and enjoy solving complex technical challenges.