Senior AI Data Engineer - GPSC Insights Engineering

Job Description This role is categorized as hybrid. This means the successful candidate is expected to report to Austin, TX three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. The Role Join our GPSC Insights Engineering team, responsible for ideating, incubating, and delivering new data solutions for global purchasing supply chain (GPSC) and our partners. As a Senior AI Data Engineer, you will design, build, and optimize industrialized data assets, AI data products, and data platforms that support Business Intelligence, Advanced Analytics, machine learning, and generative AI use cases. You will own solutions from discovery through production, ensuring that data used for training, evaluation, retrieval, inference, and decisioning is trusted, scalable, secure, observable, and fit for purpose. In this role within the GPSC Insights Engineering organization under the Data organization, you will independently lead complex initiatives with multiple moving parts and drive delivery across teams. You will apply deep data engineering and software engineering expertise to solve novel problems, shape architecture and technology strategy, influence partners through data and clear recommendations, and deliver solutions with significant impact across the function. What You’ll Do • Own the architecture, design, development, and production delivery of scalable data and AI engineering solutions across batch, streaming, and near-real-time workloads. • Assemble, curate, and transform large structured, semi-structured, and unstructured datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference. • Design and implement robust source-system integrations from files, database extracts, APIs, event streams, external suppliers, and supply-chain systems. • Build high-quality data pipelines and reusable data products with strong data contracts, schema management, lineage, metadata, quality checks, and observability. • Develop and operationalize feature, embedding, and vector-retrieval pipelines where appropriate for machine learning and generative AI applications. • Build AI-enabled data and application workflows using appropriate AI development frameworks and services, applying retrieval, evaluation, and workflow orchestration patterns where appropriate. • Establish repeatable MLOps/LLMOps engineering patterns, including data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers. • Design for functional and non-functional requirements, including performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment. • Lead architecture discussions and technology evaluations; identify tradeoffs, propose alternative approaches, and drive decisions for complex problems with limited precedent. • Identify and implement process improvements, automation, infrastructure redesign, and developer tooling that increase delivery speed and reduce operational risk. • Partner with data scientists, ML engineers, product owners, business stakeholders, platform teams, cybersecurity, and governance partners to translate customer needs into actionable technical plans. • Work with operations teams to resolve production issues, lead root-cause analysis, and implement preventative improvements across data and AI platforms. • Communicate recommendations, risks, progress, and results to technical and non-technical audiences, using data to demonstrate value and address concerns. • Mentor peers and junior engineers, provide guidance on difficult technical issues, and raise engineering standards through design reviews, documentation, and knowledge sh