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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Epitec, Inc. - **Location:** Austin, TX, United States (Remote available) - **Salary:** $124,800.0 - $135,200.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, Big Data, Continuous Integration, Programming Tools, Github, Python (Programming Language), Search Technologies, Systems Architecture, Web Application Frameworks, Software Organization, Retrieval-Augmented Generation, Multi-Agent Systems, Generative AI, Agentic-AI, AI Platforms, Model Context Protocol, Databricks - **Published:** October 3, 2026 - **Apply:** https://www.dice.com/job-detail/1430e621-580c-4ba8-83a6-28e7ab055124 ## About the Role * Strong Python development experience. * Experience with GitHub and modern software development practices. * Hands-on experience with AI frameworks such as LangChain. * Experience building Retrieval-Augmented Generation (RAG) applications. * Experience working with vector databases and semantic search. * Experience developing AI agents and workflow automation solutions. * Knowledge of MCPs (Model Context Protocols). * Experience with Harness Engineering or CI/CD automation practices. * Strong communication and documentation skills. Preferred Qualifications * AWS cloud experience. * Experience with Databricks or large-scale data platforms. * Multi-agent system architecture and development experience. * Experience building internal AI platforms, developer tools, or enablement solutions. * Experience scaling AI solutions across enterprise environments. What Success Looks Like * Increased adoption of AI platforms and resources. * Growth of reusable AI agents, skills, and templates. * Faster onboarding and knowledge sharing. * Reduced duplication of work through reusable solutions. * Improved accessibility and discoverability of AI capabilities. Ideal Candidate The ideal candidate has hands-on experience building production AI applications and agent-based solutions using modern frameworks, vector databases, and RAG architectures. They enjoy creating reusable tools and platforms that enable others to adopt AI more effectively while balancing technical development, documentation, and cross-functional collaboration. ## Description Our client is building an AI Enablement program focused on accelerating AI adoption through reusable tools, agents, workflows, and knowledge assets. They are seeking an AI Engineer to develop and maintain shared AI capabilities that can be leveraged across multiple teams, helping turn successful AI use cases into scalable solutions. This role is ideal for someone who enjoys building AI applications, agent frameworks, developer tools, and reusable platforms that make AI easier to adopt across an organization. Responsibilities * Build and maintain a centralized AI Skills, Agents, and Templates Hub. * Develop reusable AI agents, skills, workflows, and starter templates. * Design and implement RAG-based solutions and AI-powered applications. * Build integrations using MCP (Model Context Protocol) architectures. * Partner with AI Enablement leaders and AI coaches to scale successful AI solutions. * Create technical documentation, onboarding content, and implementation guides. * Manage intake, prioritization, and tracking of AI enablement requests. * Monitor adoption metrics and maintain quality of AI assets. * Coordinate solution releases and continuous improvements. * Improve discoverability and reuse of AI capabilities across teams. ## Related Videos - [Hiring AI Native Talents](https://www.wearedevelopers.com/videos/100268-hiring-ai-native-talents) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [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)