We are seeking a highly motivated and experienced Generative AI Operations (GenAI Ops) Engineer to join our innovative team. In this role, you will be at the forefront of the AI revolution, responsible for building, deploying, and maintaining the operational infrastructure for our cutting-edge generative AI models and services. You will work closely with data scientists, machine learning engineers and software developers to ensure our GenAI applications—especially complex multi-agent systems—are scalable, reliable and efficient across major cloud platforms. If you are passionate about operationalizing large-scale AI systems and want to make a significant impact, this is the role for you. Responsibilities Build and Manage CI/CD Pipelines: Design, implement and maintain robust, automated CI/CD pipelines for training, evaluating and deploying large language models (LLMs) and AI agents Orchestrate Agentic AI Workflows: Design, deploy and manage sophisticated multi-agent systems Ensure seamless Agent-to-Agent (A2A) communication and collaboration between specialized agents to automate complex business processes Manage Tool Integration: Implement and manage secure, scalable integrations between AI agents and external tools/APIs, leveraging open standards like the Model Context Protocol (MCP) to ensure interoperability Leverage AI-Powered Development: Utilize AI-powered development tools to accelerate the entire software development lifecycle from writing infrastructure code and tests to troubleshooting operational issues in cloud environments Infrastructure as Code (IaC): Utilize cloud-native IaC services or cloud-agnostic tools like Terraform to define and manage the infrastructure required for GenAI workloads Model Monitoring and Observability: Implement comprehensive monitoring and logging solutions to track model and agent performance, resource utilization and system health For agentic systems, this includes tracing the agent's actions and logging the multi-step conversational flow Scalability and Performance Optimization: Design and implement scalable architectures for model serving and inference Continuously optimize the performance and cost-effectiveness of our GenAI services Security and Compliance: Implement and enforce security best practices for our GenAI infrastructure and data Ensure compliance with industry standards and regulations Requirements 3+ years in a DevOps, SRE or MLOps role with a focus on cloud infrastructure and a background in cloud services (AWS, GCP, Azure) Skilled in building and managing CI/CD pipelines (Jenkins, GitLab CI or cloud-native services) and proficiency in at least one scripting language (e.g. Python, Bash) Familiarity with IaC tools (e.g. AWS CDK, CloudFormation, Terraform) and in containerization and orchestration (Docker, Kubernetes) Track record deploying and operating LLM inference (e.g. vLLM, Triton, TGI, Ray Serve, KServe/Seldon) Hands-on with LLM/app tracing and metrics (e.g. OpenTelemetry + Langfuse, Arize Phoenix, WhyLabs) and building eval pipelines (offline/online regression suites) Skilled in operating retrieval pipelines: embedding generation, indexing/refresh strategies, vector DBs (Pinecone, Weaviate, Milvus, FAISS) and relevance monitoring Practice running multi-agent workflows (LangGraph, CrewAI, AutoGen-like), including state management, retries, rate limits, tool-failure handling and step-level auditing Experience in implementing guardrails: secrets isolation, tool/API permissions, prompt-injection defenses, data leakage prevention, PII redaction and policy enforcement Fluent English (B2+ level) Nice to have Master's degree or PhD in Computer Science, AI, Machine Learning or a related field Background integrating agents with external tools using MCP (or similar tool-calling standards) and operating tool registries Experience with cloud-native GenAI services like AWS Bedrock, Azure AI Foundry or Google Vertex AI Familiarity with the architecture and operational challenges of Large Language Models (LLMs) Experience designing or managing multi-agent systems or complex orchestrated workflows Knowledge of monitoring and observability tools like Prometheus, Grafana or Datadog Relevant cloud or DevOps certifications Strong problem-solving skills and the ability to work effectively in a fast-paced collaborative environment
