Role Description We are looking for an experienced and ambitious builder to join our Elastic IT team in the role of Cloud Engineer. In this role, you will be a primary contributor to the IT - GenAI roadmap. You will partner across IT to identify, implement and manage a portfolio of GenAI solutions that will be leveraged across our organization. This is an exciting opportunity to be a primary contributor to our AI strategy, helping design and implement AI infrastructure, custom solutions and third party SaaS offerings. The IT Team at Elastic is seeking a talented Cloud/GenAI Engineer to strengthen our internal infrastructure and accelerate our AI initiatives. As a key member of our IT organization, you'll be responsible for architecting, implementing, and scaling cloud-native infrastructure while driving our generative AI capabilities. This role combines advanced DevOps engineering with cutting-edge AI infrastructure management. The ideal candidate brings deep technical expertise in distributed systems, container orchestration, and infrastructure automation, with a proven track record in building resilient, scalable cloud platforms. You'll be working in a highly-distributed team environment, collaborating with global IT professionals to drive innovation and operational excellence. What You Will Be Doing - Develop comprehensive infrastructure as code using Terraform, including custom providers and modules - Implement configuration management using Ansible, including custom roles and playbooks - Create automated deployment pipelines with advanced CI/CD practices (GitOps, trunk-based development) - Design and implement infrastructure testing frameworks and validation procedures - Implement comprehensive observability solutions using the Elastic Stack - Design and maintain logging architectures with log aggregation and analysis - Implement security controls and compliance measures across the infrastructure - Manage secrets and certificates using HashiCorp Vault and cert-manager - Manage and optimize containerized environments using Kubernetes, including custom resource definitions (CRDs) and operators - Implement advanced Kubernetes features like HPA/VPA, network policies, and pod security policies - Design and maintain container image build pipelines with security scanning and optimization - Design and implement highly available, fault-tolerant cloud infrastructure supporting internal systems and GenAI applications - Architect multi-region Kubernetes clusters with advanced networking and security configurations - Implement service mesh architectures for microservices communication and traffic management - Design and maintain GitOps workflows for infrastructure and application deployment Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - Proven experience in developing generative AI models, including Natural Language Processing (NLP) or computer vision models. - Proficiency in deep learning frameworks such as Microsoft Cognitive Services, TensorFlow, PyTorch, or Hugging Face Transformers. - Strong knowledge of cloud platforms and services (e.g., AWS, Azure, GCP). - Experience with containerization and orchestration (e.g., Docker, Kubernetes). - Infrastructure as Code (Terraform, CloudFormation, ARM templates) - Configuration management (Ansible, Salt) - Advanced networking expertise, including overlay networks (especially VPNs), VPC configuration and management, and load balancing for performance and reliability. - GitOps workflows (ArgoCD, Flux) - CI/CD platforms (Jenkins, GitLab CI, GitHub Actions) - Advanced Git workflows and branching strategies - Experience implementing configuration as code practices beyond infrastructure provisioning, such as managing application and service configurations using tools like Ansible, Salt, or similar. - Ability to design reusable, modular configuration templates that support rapid deployment and consistent environments. - Hands-on experience with Elasticsearch, including cluster management, index lifecycle policies, and query optimization. - Ability to design and maintain scalable search and analytics solutions using the Elastic Stack. - Familiarity with integrating Elasticsearch into observability, logging, and monitoring workflows is highly valued. - Proficiency in building and customizing dashboards using Kibana or other visualization tools. - Ability to translate complex data into actionable insights for technical and non-technical stakeholders. - Experience with dashboard automation and templating is a plus. - Knowledge of DevOps practices for model deployment and automation. - Strong problem-solving skills and the ability to work in a dynamic and fast-paced environment. - Excellent communication and collaboration skills. - Familiarity with ethical AI practices and responsible AI development. - Strong experience with major cloud platforms (AWS, GCP, or Azure) Benefits - Competitive pay based on the work you do here and not your previous salary - Health coverage for you and your family in many locations - Ability to craft your calendar with flexible locations and schedules for many roles - Generous number of vacation days each year - Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service - Up to 40 hours each year to use toward volunteer projects you love - Embracing parenthood with minimum of 16 weeks of parental leave

• Designing and enhancing AI-powered chatbots for customer interactions in billing, sales, and technical support to reduce reliance on human agents. • Automating business processes to improve operational efficiency using AI technologies. • Contributing to the development and maintenance of scalable AI infrastructures and integrate new machine learning techniques and large language models (LLMs) where applicable. • Evaluating and working with various AI models including LLMs, reasoning models, machine learning, and function calling models for different purposes. • Contributing to the roadmap for integrating speech-to-text functionality and real-time voice APIs, especially for telephony systems. • Working closely with other team members to evaluate and implement new AI technologies and tools, ensuring robust and reliable AI systems. • Participating in the testing and quality assurance of AI-driven systems, ensuring accurate and efficient performance. • Managing and maintaining AI agents across various platforms, ensuring their smooth operation and continuous improvement.

• Partner with DnA teammates, software engineers and business stakeholders to deliver high-impact, AI-enabled solutions • Create AI capabilities into internal tools, customer-facing products and operational workflows using APIs, cloud services and modern software development practices • Implement secure and compliant AI systems that align with company policies, regulatory expectations and responsible AI principles • Architect/design, build and productionalize AI and machine learning solutions including generative AI, natural language processing, predictive modeling and automation workflows • Develop modularized/templatized code structures; new requests are config changes not rewrites • Help mentor early career data scientists and AI engineers • Evaluate multiple LLM providers (Anthropic, Google, OpenAI) and determine the best fit for each use case based on quality, latency, cost and reliability • Establish monitoring and feedback loops for continuous improvement • Document technical decisions, system architectures and model assumptions • Stay current on emerging AI tools, frameworks, model capabilities and industry best practices; recommend and implement pragmatic adoption strategies

• Work with team members on diverse projects and tasks. • Perform research and analysis to aid project objectives. • Create reports, presentations, and necessary documentation. • Engage in team meetings and share ideas. • Learn and implement industry best practices and company standards. • Support basic development and testing of AI chatbot features, including prompts, data inputs, and user interactions. • Help troubleshoot and improve chatbot performance by assisting with debugging, evaluations, and simple integrations. • Collect, clean, and preprocess large-scale manufacturing datasets. • Apply machine learning models (e.g., regression, classification, clustering) to identify trends, anomalies, and predictive insights. • Collaborate with engineers and operations teams to translate data findings into actionable recommendations. • Develop visualizations and dashboards to communicate results clearly to technical and non-technical stakeholders.