• Evolve a modern, AI-first quality strategy for our fast-scaling SaaS architecture, including foundational infrastructure and emerging agentic/intelligent systems. • Integrate AI enhancements into CI/CD pipelines (e.g., predictive flakiness detection, automated test generation, self-healing scripts) to improve isolation, data setup, & execution reliability using existing/suggesting tools. • Establish scalable testing practices that support hyper-growth and petabyte-scale AI data pipelines. • Design deterministic and statistical testing approaches for non-deterministic LLM-based and agentic systems, addressing hallucinations, prompt injection, bias, drift, and safety risks. • Build automated evaluation pipelines and harnesses for correctness, faithfulness, retrieval quality, generation accuracy, tool-calling, planning sequences, and multi-agent flows. • Execute/Develop test frameworks for the full AI lifecycle: prompts, datasets, embeddings, model versions, RAG pipelines (end-to-end validation), and guardrails. • Implement red-teaming, bias/fairness checks, and compliance mechanisms; leverage in-trend frameworks for metrics and observability. • Partner closely with product, data science, AI engineering, and dev teams to test AI features, conduct multi-agent simulations, and ensure high-quality roadmap delivery. • Facilitate knowledge sharing and upskilling on AI testing best practices across the Quality Function. • Drive core metrics (DORA, test coverage/effectiveness) plus AI-specific indicators (e.g., hallucination rate, context precision, drift detection). • Build real-time dashboards and support A/B testing of models with post-deployment monitoring. • Champion a quality-first, ethical AI mindset organization-wide. • Mentor SDET’s, lead workshops on AI risks/validation, and influence design/deploy/incident processes. • Define roadmaps and best practices for sustainable AI quality assurance.

• Lead engineering effort for new and ongoing substation projects. • Support multiple projects simultaneously. • Work closely with NV5 Substation Engineering leadership to ensure project success. • Prepare conceptual designs and execution plans. • Perform and document quality reviews for other engineers and designers. • Prepare construction drawings, documents, calculations, and studies for medium and high voltage substation projects. • Responsible for technical delivery of power delivery engineering projects or tasks. • Prepare calculations, studies, and reports for substation and power systems projects meeting internal and client standards and requirements.

• Own the architecture and evolution of our large-scale data extraction and normalization platform • Design systems that process hundreds of thousands of records across heterogeneous sources (PDF, HTML, APIs, XLS) with high reliability • Define and implement strategies to minimize human-in-the-loop validation through AI-based validation, confidence scoring, provenance tracking, and deterministic safeguards • Establish clear system contracts for correctness, traceability, and confidence (e.g., “where did this number come from?”) • Balance AI-driven approaches with procedural and rules-based systems where they improve reliability and explainabilityIdentify and remediate architectural and operational bottlenecks impacting scale, accuracy, and developer velocity • Act as the technical authority to block poor designs, redesign critical systems, and introduce new platforms or tooling when necessary • Partner with product and downstream consumers to define quality bars, SLAs, and success metrics • Serve as a technical escalation point for production issues, reliability failures, and systemic risks • Mentor senior engineers, shape technical culture, and raise expectations for system design and execution • Contribute to long-term technical strategy while remaining hands-on with critical implementations

• Own the architecture and evolution of our large-scale data extraction and normalization platform • Design systems that process hundreds of thousands of records across heterogeneous sources (PDF, HTML, APIs, XLS) with high reliability • Define and implement strategies to minimize human-in-the-loop validation through AI-based validation, confidence scoring, provenance tracking, and deterministic safeguards • Establish clear system contracts for correctness, traceability, and confidence (e.g., "where did this number come from?") • Balance AI-driven approaches with procedural and rules-based systems where they improve reliability and explainabilityIdentify and remediate architectural and operational bottlenecks impacting scale, accuracy, and developer velocity • Act as the technical authority to block poor designs, redesign critical systems, and introduce new platforms or tooling when necessary • Partner with product and downstream consumers to define quality bars, SLAs, and success metrics • Serve as a technical escalation point for production issues, reliability failures, and systemic risks • Mentor senior engineers, shape technical culture, and raise expectations for system design and execution • Contribute to long-term technical strategy while remaining hands-on with critical implementations