Lead Generative AI Operations Engineer (GenAI Ops)

Generative AI Operations

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

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