Generative AI Operations Engineer (GenAI Ops)

We are seeking a highly motivated and experienced Generative AI Operations (GenAI Ops) Engineer to join our forward-thinking team. In this position, you will play a key role in building, deploying, and maintaining the operational backbone for advanced generative AI models and services. You will collaborate with data scientists, machine learning engineers, and software developers to ensure our GenAI applications—including complex, multi-agent systems—are scalable, reliable, and efficient across leading cloud platforms. If you are passionate about operationalizing large-scale AI systems and eager to make a meaningful impact, we want to hear from you. Responsibilities Design, implement, and maintain automated CI/CD pipelines for training, evaluating, and deploying large language models (LLMs) and AI agents Deploy and manage sophisticated multi-agent AI systems, ensuring seamless agent-to-agent communication and collaboration for automating complex business processes Integrate AI agents with external tools and APIs, using open standards like Model Context Protocol (MCP) to ensure interoperability and security Utilize AI-powered development tools to accelerate infrastructure code, testing, and troubleshooting in cloud environments Define and manage GenAI infrastructure using cloud-native or cloud-agnostic Infrastructure as Code (IaC) tools such as Terraform Implement monitoring and logging solutions to track model and agent performance, resource usage, and system health, including tracing agent actions and multi-step conversational flows Design and optimize scalable architectures for model serving and inference, focusing on performance and cost-effectiveness Apply security best practices and ensure compliance with industry standards and regulations for GenAI infrastructure and data Requirements Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience At least 3 years of experience in a DevOps, SRE, or MLOps role with a focus on cloud infrastructure Hands-on experience with cloud services from major providers such as AWS, Google Cloud, or Azure Strong background in building and managing CI/CD pipelines using tools like Jenkins, GitLab CI, or cloud-native solutions Proficiency in at least one scripting language such as Python or Bash Experience with Infrastructure as Code (IaC) tools like AWS CDK, CloudFormation, or Terraform Familiarity with cloud-native GenAI services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI Understanding of the architecture and operational challenges of Large Language Models (LLMs) Practical experience with generative AI frameworks including Hugging Face, OpenAI, or LangChain Experience with monitoring and observability tools for AI/ML systems such as Prometheus or Grafana Experience with ML experiment tracking and versioning tools like MLflow or Weights & Biases Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes Fluent English communication skills at a B2+ level Nice to have Master’s degree or PhD in Computer Science, AI, Machine Learning, or a related discipline Experience designing or managing multi-agent systems or orchestrated AI workflows Relevant certifications in cloud or DevOps technologies Strong analytical and problem-solving skills with the ability to thrive in a fast-paced, collaborative environment