AI Tooling Engineer

pWe are looking for an AI Tooling Engineer to develop and scale an internal agentic platform that brings together company knowledge, data, and tools in one secure, accessible environment. Working closely with a Principal Engineer, you will build production-ready AI agents and integrations that help employees and their AI assistants access the information they need. You will take ownership of the full development lifecycle, from understanding user needs and designing solutions to deployment, monitoring, and continuous improvement. Using technologies such as Python, LangGraph, LLM APIs, and Model Context Protocol (MCP), you will transform prototypes into reliable tools used across the organization. You will also establish evaluation frameworks, optimize AI performance and costs, and ensure robust security and access controls. This remote opportunity offers the chance to shape company-wide AI capabilities within a globally distributed, technology-driven team. /p h3Accountabilities /h3 pYou will help develop and maintain an internal AI platform, combining agent development, systems integration, production engineering, and continuous optimization to deliver practical tools that improve how teams work. /p ul liCollaborate with the Principal Engineer to design, develop, and deploy new platform capabilities, taking ownership from initial design through production release. /li liIntegrate internal systems and SaaS applications by connecting APIs and data sources, defining shared data models, and implementing appropriate access permissions. /li liBuild AI agents and supporting tools, including MCP servers that enable agents across the organization to interact with internal systems and retrieve relevant context. /li liDesign and improve retrieval workflows, tool calling, embeddings, prompts, and context management to deliver accurate and useful AI-generated responses. /li liEstablish and maintain AI quality standards through evaluation datasets, benchmarks, regression testing, tracing, and observability. /li liMonitor performance indicators, including cost per answer and token usage, and optimize models, prompts, and workflows for efficiency and reliability. /li liTroubleshoot production issues, incorporate user feedback, and continuously improve the platform based on real-world usage. /li liTurn proofs of concept into secure, scalable cloud applications with appropriate infrastructure, deployment processes, and identity and access controls. /li liUse AI coding agents as a core part of the development workflow while maintaining code quality through clear specifications, automated tests, reviews, and sound engineering practices. /li liContribute to company-wide AI tooling initiatives, including evaluating and standardizing internal AI tools, tracking usage and costs, and defining standards for AI-generated applications. /li liSupport other teams in adopting and building on the platform, helping them develop effective AI-powered workflows and internal applications. /li liEngage with technical and non-technical stakeholders across the organization to understand their needs, prioritize improvements, and communicate solutions clearly. /li /ul h3Requirements /h3 pThe ideal candidate is a hands-on software engineer with practical experience delivering AI agents to production, a strong understanding of modern AI development frameworks, and the ability to build secure, reliable internal tools. /p ul liAt least 3 years of experience building and shipping production software, primarily using Python and relational databases. /li liProven experience building and deploying at least one production AI agent with LangGraph that is used by real users. /li liPractical expertise in agent development, including tool calling, retrieval and embeddings, prompt engineering, and context design. /li liHands-on experience with LLM APIs from major providers, including token management and cost optimization. /li liExperience working with MCP or comparable tool protocols, both as a server and as a client. /li liStrong understanding of AI evaluation methodologies, including test datasets, LLM-as-judge approaches, regression testing, and performance monitoring. /li liFamiliarity with tracing and observability tools such as Langfuse or equivalent solutions. /li liExperience integrating APIs and data sources, including SaaS applications, SQL databases, and data warehouses. /li liPractical experience with cloud infrastructure on Google Cloud Platform (GCP), Amazon Web Services (AWS), or both, including containers, managed databases, identity and access management, and CI/CD pipelines. /li liDaily use of AI coding agents, with the ability to validate their output and maintain high standards of software quality. /li liStrong understanding of security principles, identity management, permissions, and least-privilege access controls. /li liExperience with version control and collaborative development tools such as GitHub or Bitbucket. /li liExcellent writt