Senior AI Engineer I

RWE•Austin, TX

1d•$127,000 - $171,000•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

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