Role Description We are hiring a Staff AI Engineer to lead the technical design of TaskRay's AI platform. This role sits at the center of our next 18 months: an off-platform agent layer that integrates deeply with our Salesforce-native distribution, productized through outcome-based packaging and validated through engagements with our design partner customers. - Own the architecture, set the engineering standards for how we build with LLMs. - Partner directly with our VP of Product & Engineering, our Architect, and our senior Salesforce engineering lead to ship. - Be the technical voice in design partner work to learn what to productize. - Sequence agents deliberately, starting with internal-facing capabilities before extending to the External Onboarding Agent. - This is a builder's role with leverage; your technical judgment will shape what every other engineer on the team builds. Qualifications - Track record of shipping production agent systems at meaningful scale. - Deep, hands-on experience designing agent systems in production. - Strong Python proficiency with production experience deploying real systems. - Hands-on experience with Anthropic Claude or comparable LLM provider APIs at production scale. - Demonstrated technical leadership in scoping work and making architectural decisions. - Comfort in customer-facing technical discovery and design partner contexts. Requirements - Architecture and Technical Strategy - Own the architecture of TaskRay's off-platform AI tier. - Make and defend build versus buy decisions across the agent stack. - Partner with our Architect and senior Salesforce engineering lead on the data contract. - Translate the company's AI strategy into a buildable technical plan. - Agent and AI Systems - Architect and lead the build of our MCP server. - Design and lead the build of our PM Agent. - Design and lead the build of our Execution Agent. - Design and lead the build of our External Onboarding Agent. - Build the eval, observability, and feedback infrastructure. - Set our approach to retrieval, tool use, and multi-step reasoning. - Design Partner Engagements - Partner with design partner customers to discover repeatable workflow patterns. - Lead the technical scoping for design partner work. - Be the technical voice in those engagements alongside Product. - Engineering Leadership and Standards - Establish TaskRay's engineering standards for AI feature development. - Actively contribute to our agentic coding standards and norms. - Mentor senior and mid-level engineers contributing to AI work. Benefits - Medical, dental, and vision benefits. - Every other Friday off and a team that respects your time outside of work. - Flexible PTO. - 12 weeks paid family and medical leave, 16 weeks for birthing people. - Vacation bonuses. - Anniversary bonuses. - Company-paid life insurance. - 401(k) matching. - Cell phone reimbursement stipend. - Employee Assistance Program.

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