> Markdown version of [/jobs/ext/4626800-data-engineer-ai](https://www.wearedevelopers.com/jobs/ext/4626800-data-engineer-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer (AI) - **Company:** Techstars LLC - **Location:** Atlanta, GA, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Hospital Information Systems, Information Engineering, Data Infrastructure, Data Systems, Python (Programming Language), Operational Databases, SQL Databases, Data Streaming, Systems Integration, Large Language Models, Apache Spark, Reliability of Systems, Machine Learning Operations, Data Pipelines, Automation Anywhere - **Published:** November 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/data-engineer-ai-atlanta-ga--3632fd32-5b92-4d8e-9a60-bcfa1281f79c ## About the Role Data Engineering Experience Strong experience building and maintaining production data pipelines. Proficiency in Python, SQL, and modern data platforms. Experience with Spark or comparable distributed processing systems. Demonstrated ability to operationalize and support data systems or models in production. Strong troubleshooting skills across data flows and system boundaries. AI and Analytical Judgment Practical experience working with LLMs, integrating AI tools, or supporting AI/ML workflows. Understanding of LLM fundamentals and the implications of probabilistic outputs. Experience using AI tools to accelerate development and problem-solving while independently validating results. Sound statistical intuition and the ability to reason about data quality, anomalies, and output reliability. Ownership and Collaboration A product mindset: you connect technical work to user needs and business outcomes. A habit of testing assumptions and validating outputs. Pragmatic judgment about when to build, reuse, or simplify. Comfort working through ambiguity in a rapidly evolving technical environment. Clear, proactive communication and strong follow-through. Ability to explain your approach and work effectively across Product, Engineering, and Data teams. ## Description We're hiring a Staff Data Engineer with a strong AI orientation to help build and scale the company's data and AI platform. This is a hands-on role with substantial ownership across data pipelines, production AI workflows, and system reliability. You will take initiatives from loosely defined problems through implementation, validation, and ongoing operation. The right person combines strong data engineering fundamentals with practical AI experience, sound judgment, and a product mindset. You care about whether a system produces reliable, useful results-and take responsibility for making it work in production. What You Will Own Data Engineering and Reliability Design, build, and maintain scalable batch and real-time data pipelines. Improve data quality, consistency, and reliability across systems. Resolve gaps between upstream data sources and downstream applications, with clear expectations for data behavior and ownership. Diagnose pipeline failures, performance bottlenecks, and unexpected data changes. Production AI Systems Partner with Product and Engineering to deliver AI-enabled functionality. Deploy, monitor, and troubleshoot LLM-based and machine learning workflows in production. Turn models and experimental workflows into maintainable systems. Evaluate outputs for reliability and usefulness, and address failure patterns before they affect downstream users. Validation and Operational Excellence Build validation frameworks, automated checks, and monitoring for data and AI output quality. Apply statistical reasoning to identify anomalies, question assumptions, and investigate unexpected results. Improve observability, performance, and operational reliability. Establish practical safeguards for systems whose outputs can vary. Technical Ownership and Execution Translate ambiguous business and product needs into clear technical solutions. Make pragmatic tradeoffs among speed, complexity, cost, and quality. Use existing platforms and AI development tools where they improve execution. Communicate technical decisions clearly and follow through from implementation to production support.