Overview

We are creating an AI-native platform that helps engineering teams execute the software delivery lifecycle with speed, quality, and control - from planning and implementation to testing, release, governance, and production feedback.

Responsibilities

• Build and maintain production-grade MLOps and LLMOps pipelines for AI applications.
• Automate model training, evaluation, versioning, deployment, monitoring, and rollback workflows.
• Build CI/CD pipelines for AI/ML models, services, and supporting infrastructure.
• Deploy and operate AI workloads across cloud, containerised, and Kubernetes environments.
• Build reliable model-serving and inference infrastructure with a focus on performance, scalability, and cost.
• Implement monitoring and observability for model performance, latency, reliability, and infrastructure health.
• Work with AI Engineers to optimise models and inference workloads for production.
• Manage data, model, and feature pipelines across development, staging, and production.
• Troubleshoot production issues across AI applications, models, data pipelines, and infrastructure.
• Establish best practices for reproducibility, security, reliability, and governance of AI systems.
• Evaluate and adopt emerging AI infrastructure, MLOps, and LLMOps technologies.
• 1-3 years of experience in MLOps, ML Engineering, AI Infrastructure, DevOps, SRE, or Platform Engineering.
• Strong software engineering and automation skills.

Qualifications

• Strong understanding of cloud infrastructure, containers, distributed systems, and deployment workflows.
• Strong problem-solving and production troubleshooting skills.
• Ability to collaborate effectively with AI, backend, platform, and product teams.
• Strong ownership mindset and willingness to work in a fast-moving environment.
• Genuine interest in AI, LLMs, AI agents, and production AI systems.
• Strong proficiency in Python, Go, or Bash.
• Hands-on experience with AWS, Azure, or Google Cloud.
• Understanding of model training, evaluation, versioning, deployment, and monitoring.
• Familiarity with LLMOps, RAG, embeddings, vector databases, and AI agent workloads.
• Strong understanding of Linux, networking, cloud security, IAM, and containerised systems.
• Build the infrastructure behind the next generation of AI-powered software engineering.
• Work at the intersection of AI, ML, cloud infrastructure, and distributed systems.
• Solve challenging problems around model reliability, inference, scalability, observability, and cost.
• Work closely with AI, backend, and platform engineers.
• Take ownership of systems from architecture and automation through production.
• Work with modern AI and cloud technologies and continuously expand your technical expertise.
• Join a high-ownership AI-first company where your work directly shapes the engineering foundation.
• Grow with Revolte as we build technology that changes how software engineering is done.