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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer I - **Company:** RWE Americas - **Location:** Austin, TX, United States - **Experience:** Expert - **Salary:** $127,000.0 - $171,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Component-Based Software Engineering, Cluster Analysis, Code Review, Continuous Integration, Enterprise Content Management, Python (Programming Language), Machine Learning, Regression Testing, SQL Databases, Cloud Platform System, Large Language Models, AI Platforms, Information Technology, Software Version Control - **Published:** September 24, 2026 - **Apply:** https://www.austinjobsite.com/job.asp?id=3402983191&tx=KP6565FFJ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Bachelor's degree in computer science, data science, another STEM field, or a related discipline required * Minimum 5 years of relevant experience in software or AI/ML engineering roles, including demonstrated ownership of AI or ML capabilities end to end, from design through operation in a production environment * Depth in two or three of the capability areas listed under Basic Function, with working familiarity across the rest * Production experience with Python and SQL, a major cloud platform, version control and code review, containerized deployment, and CI/CD for AI products and services * Hands-on experience with current orchestration, agent, and retrieval frameworks * Practical experience developing ML models for supervised and unsupervised problems * Experience building and maintaining evaluation and regression testing for AI and ML systems * Working knowledge of AI-specific security and responsible AI controls * Competent use of coding agents and other AI-assisted tooling, including scoping the right tasks for them, reviewing generated code, and recognizing which tasks are a poor fit for delegation * Experience with IT/OT environments, industrial data sources, or operational technology systems is preferred * Experience in the power, utilities, or clean energy sector, including knowledge of U.S. power markets, renewable project development, and clean energy technologies, is preferred ## Description The Senior AI Engineer I is a hands-on engineer accountable for end-to-end AI capabilities within a product or across multiple products, from design and build through operation in production. Scope includes retrieval and context assembly, model and agentic logic, integration with source systems and consuming applications, monitoring and evaluation, and operational runbook maintenance. Depth is expected in two or three of these capability areas, with working familiarity across the rest. The role also covers the engineering that makes these capabilities trustworthy in business use. This includes evaluation and regression testing, human-in-the-loop and other AI-specific security controls, and accountability for inference cost and latency. The role builds on the shared platform services and reference implementations set by the Principal AI Engineer, within the target-state architecture set by the Data/AI Platform Architect. It also takes handover of capabilities built by technical delivery partners and maintains them in production. Role Responsibilities: * Design, build, operate, and own end-to-end AI capabilities within a product or across multiple products, spanning retrieval and context assembly; model and agentic logic; integration with source systems and consuming applications (APIs, MCPs, authentication); monitoring and evaluation; and operational runbook maintenance * Train and evaluate machine learning models for well-scoped problems such as classification, regression, anomaly detection, clustering, and forecasting, using operational and commercial data. Determine when and how to use ML models versus simpler statistical baselines * Build retrieval and context pipelines against enterprise content using chunking, tagging, and a metadata strategy. Measure and report retrieval quality against a defined standard * Design and maintain evaluation for these capabilities, including offline evals, automated scoring such as LLM-as-judge, and regression tests that run on every prompt or pipeline change to ensure quality before release. Monitor quality and drift signals in production * Own inference cost and latency in production, tracking cost per transaction and end-to-end response time, and profiling where that time is spent. Make tradeoffs where needed to stay within agreed targets * Implement human-in-the-loop and AI-specific security controls, including shadow and assisted modes, confidence and citation surfaces, override capture, audit trails, identity-scoped retrieval, input and output guardrails, tool permissions, and prompt injection resistance * Take handover of application components built by technical delivery partners: read the code, close operational gaps, update the runbook, and maintain them in production * Improve team efficiency by establishing reusable components and reference examples, reviewing code, and onboarding new engineers ## Related Videos - [This App Reached 10,000 Users in One Week. Here's How.](https://www.wearedevelopers.com/videos/100329-this-app-reached-10-000-users-in-one-week-here-s-how) - [Are Code Reviews Worth It? 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