Lead Generative AI Operations Engineer (GenAI Ops)
We are seeking a highly skilled Generative AI Operations Engineer (GenAI Ops) to join our cutting-edge AI team. The ideal candidate will have strong expertise in operationalizing large-scale generative AI systems, building CI/CD pipelines, and managing AI agent infrastructures across cloud environments. You will play a key role in ensuring the scalability, security, and performance of multi-agent AI systems and generative applications.
Responsibilities
- Design, implement, and maintain automated CI/CD pipelines for the development, training, and deployment of Large Language Models (LLMs) and AI agents
- Build and manage agentic AI systems, ensuring efficient agent-to-agent collaboration and orchestration of complex workflows
- Integrate AI agents with external tools and APIs using modern standards such as the Model Context Protocol (MCP)
- Leverage AI-powered development tools to streamline software delivery, infrastructure management, and troubleshooting processes
- Define and manage cloud infrastructure for GenAI workloads using Infrastructure as Code (IaC) tools such as Terraform, AWS CDK, or CloudFormation
- Implement monitoring and observability solutions for models, agents, and system health using tools like Prometheus, Grafana, or Datadog
- Optimize scalability, performance, and cost-efficiency of GenAI services in production environments
- Enforce AI security, safety, and governance practices, ensuring compliance with organizational and industry standards
Requirements
- Minimum 3 years of experience in DevOps, Site Reliability Engineering (SRE)
- Minimum 1 year of experience in MLOps roles with a strong focus on cloud infrastructure
- Proven experience with AWS, Google Cloud, or Azure
- Proficiency in Python or Bash, and experience with containerization/orchestration tools such as Docker and Kubernetes
- Strong background in building and maintaining CI/CD pipelines using Jenkins, GitLab CI, or similar tools
- Experience with cloud-native GenAI platforms (e.g., AWS Bedrock, Azure AI Foundry, Google Vertex AI)
- Familiarity with LLM architectures and the challenges of deploying large-scale models
- Experience designing or managing multi-agent systems and orchestrated AI workflows
- Hands-on experience implementing infrastructure using IaC frameworks
- B2+ level of English proficiency
Nice to have
- Master’s or PhD in Computer Science, AI, or related field
- Relevant cloud or DevOps certifications (e.g., AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer)
- Strong problem-solving mindset and ability to thrive in a fast-paced, innovative environment
