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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI ENGINEER - **Company:** Glendee Corp. - **Location:** Meridian, ID, United States - **Salary:** $75,000.0 - $85,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Windows, Accounting Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Big Data, Extract Transform Load (ETL), Apache Hadoop, Monitoring of Systems, Python (Programming Language), Machine Learning, Project Management Software, Mesh Networking, OAuth, Windows PowerShell, Cloud Services, Markdown, Tensorflow, Azure Machine Learning, Reverse Proxy, Search Technologies, Session Management, Microsoft SharePoint, SQL Databases, Data Streaming, Systems Integration, Talend, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Model Validation, Rate Limiting, Low Latency, Data Analytics, Graphql, Front End Software Development, Restful APIs, Pagination, Data Pipelines - **Published:** August 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b0933528f241443b ## About the Role OpenClaw or comparable agent framework. Hands-on experience building agents is required. You should be comfortable with agents and session management, the skills system, tool definitions and tool permission policy, gateway configuration, scheduled agent tasks, agent memory and context files, and sub-agent orchestration Production LLM engineering. You have built and shipped LLM-powered systems that other people depended on, including: * Tool and function calling, and designing tool interfaces a model can use reliably * Multi-step agentic workflows with planning, tool selection, and error recovery * Context management: deciding what belongs in the context window versus retrieval, and managing the associated cost and latency tradeoffs * Retrieval-augmented generation, including chunking strategy, embedding selection, hybrid search, reranking, and honest evaluation of retrieval quality * Structured output generation and schema validation- * Model selection and routing across cost, latency, and capability tradeoffsStrong * Strong Python skills. You can work confidently in a large existing codebase: reading unfamiliar code, tracing bugs across modules, writing tests, and refactoring safely. * API integration experience. Much of this work is systems integration rather than model work. You need real proficiency with REST APIs, OAuth 2.0 and token management, rate limiting, retries, pagination, and idempotency. Critically, you should be someone who verifies API behavior empirically rather than assuming the documentation is complete or current. * Diagnostic judgment. The hardest problems in this role fail silently rather than loudly: a monitor that reports healthy because it quietly stopped checking, an agent answering confidently from stale data, or a data feed that looks like a slow week but is actually broken. We need an engineer whose instinct is to ask how they know something is genuinely working, and then go prove it. PREFERRED QUALIFICATIONS Microsoft 365 and Graph API: mail, calendar, Teams, SharePoint, app registrations, and permission scopes Vector databases in production, ideally Qdrant, and practical experience with embedding models Local model hosting and serving: Ollama, vLLM, LiteLLM, and quantization tradeoffs- Mesh networking such as Tailscale, and basic reverse proxy configuration PowerShell and Windows endpoint scripting- Evaluation frameworks for measuring LLM output quality Front-end skills for internal dashboards and portals Vision and OCR work for drawing analysis and scanned document extraction ## Description MGI Inc. is hiring an AI Engineer to help build and expand our internal AI automation platform. This is not an exploratory or research position. We have a working production system in daily use across our project management, accounting, contracting, and IT departments, and we are looking for an experienced engineer to help extend it, harden it, and bring new departments online. You will design, build, and maintain AI agents that perform real work: answering employee questions from live company data, generating documents, monitoring systems, analyzing bids, and automating recurring workflows. Some agents serve general employee needs, and others are purpose-built for a single specialized task. You will create and maintain systems end to end, from the API integration through to the deployed agent that employees rely on every day. WHAT YOU WILL WORK WITH Our platform is substantial and already in production:- A multi-agent system with several named AI agents, each with its own role, tool set, and company identity, serving different departments- Roughly 200,000 lines of Python powering agent tools and integrations- Chat-based agents that employees message directly in Microsoft Teams and receive substantive answers from- A retrieval system holding millions of indexed documents, including company email and attachments, construction codes and standards, regulations, and project documentation- Deep third-party integrations including Procore, Microsoft 365 and Graph API, SharePoint, payroll and accounting systems, contracting data sources, and security tooling- Well over a hundred scheduled automation jobs handling syncs, monitoring, reporting, and alerting- A local compute fleet running vector search, model routing, and local inference alongside frontier API modelsThe platform layer is OpenClaw. Agent behavior is defined through configuration and markdown, with Python implementing the tools each agent can call. Responsibilities * Design, develop, and refine AI models using frameworks such as TensorFlow and other machine learning tools to solve complex problems. * Implement natural language processing (NLP) techniques for data extraction and analysis from unstructured data sources. * Utilize big data systems like Hadoop and Spark to process large datasets efficiently for predictive modeling analysis. * Collaborate with cross-functional teams to integrate AI models into cloud-based platforms utilizing AWS and machine learning cloud services. * Conduct statistical analysis, model training, evaluation, and validation to ensure high accuracy and robustness of AI solutions. * Develop scalable data pipelines using ETL processes, Talend, and SQL databases to support ongoing AI initiatives. * Deploy AI models into production environments with a focus on model evaluation, monitoring, and continuous improvement. ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Keeping applications secure by evolving OAuth 2.0 and OpenID Connect](https://www.wearedevelopers.com/videos/100152-keeping-applications-secure-by-evolving-oauth-2-0-and-openid-connect) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [Delay the AI Overlords: How OAuth and OpenFGA Can Keep Your AI Agents from Going Rogue](https://www.wearedevelopers.com/videos/1637-delay-the-ai-overlords-how-oauth-and-openfga-can-keep-your-ai-agents-from-going-rogue) - [GraphQL + Apollo + Next.js: A Lovely Trio](https://www.wearedevelopers.com/videos/311-graphql-apollo-next-js-a-lovely-trio) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [A 5-Step Open-Source Setup for Agentic Engineering](https://www.wearedevelopers.com/magazine/738-a-5-step-open-source-setup-for-agentic-engineering) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 210: AI Agents Are Go! 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