About the role
- Strong hands-on experience with AWS / Azure / GCP
- Expertise in Infrastructure-as-Code (Terraform preferred)
- Experience provisioning and managing production environments
- Experience building and operating ML/LLM pipelines
- Knowledge of:
- CI/CD for AI workloads
- Model & prompt versioning
- Monitoring, drift detection, retraining
- Experience designing CI/CD pipelines
- Automation of build → deploy → monitor workflows
- Hands-on with Docker and Kubernetes
- Managing scalable, distributed workloads
- Experience supporting:
- LLM-based applications (RAG, APIs, pipelines)
- Vector databases and inference systems
- Managing compute, scaling, and performance
AI-Native Engineering (Critical)
- Actively uses AI tools (LLMs, copilots, agents) in daily engineering work
- Experience working with local/open-source LLMs
- Applies AI to improve automation, debugging, and operational efficiency
- Provision and manage cloud infrastructure for AI applications
- Build and maintain MLOps / LLMOps pipelines
- Deploy and operate AI-powered applications in production
- Implement monitoring, logging, and observability
- Optimize cost, performance, and resource usage
- Ensure reliability, scalability, and security
- Collaborate with engineering teams to productionize AI systems
- 5–10+ years in DevOps / Cloud / Platform Engineering
- 2–4+ years in MLOps / LLMOps / AI platforms
- Strong experience with:
- Python / scripting
- CI/CD tools
- Infrastructure-as-Code
- Experience with vector databases
- Exposure to LLM ecosystems
- Familiarity with microservices / event-driven systems
- Knowledge of AI governance and security