Lead AI Engineer

Key Responsibilities:

AI/ML Architecture & Platform

• Define and maintain the end-to-end AI/ML architecture on Primoris' Databricks Lakehouse, including feature engineering, model training, evaluation, deployment, and monitoring pipelines.
• Design scalable, secure, and cost-optimized AI/ML and GenAI patterns that integrate with the Medallion architecture, Unity Catalog, and downstream BI and operational systems.
• Architect and implement MLflow-based model lifecycle management (experiment tracking, model registry, versioning, and deployment) across development, staging, and production environments.
• Evaluate and recommend AI/ML frameworks, foundation models, vector databases, and GenAI tooling (e.g., LangChain, LlamaIndex, Azure OpenAI, Semantic Kernel) aligned to Primoris' use cases and platform standards.
• Partner with enterprise architecture, security, and infrastructure teams to ensure AI/ML systems align with broader platform, integration, and compliance standards

Delivery & Operations
Lead the end-to-end delivery of AI/ML use cases from requirements and data exploration through model development, validation, deployment, and production monitoring.

• Serve as the first point of contact for technical challenges or escalations related to AI/ML workloads in development and production, including triage, root cause analysis, and remediation.
• Provide technical leadership and delivery oversight across hybrid teams including internal engineers, consulting partners, and offshore resources, ensuring clear accountability and consistent outcomes.
• Establish and champion AI/ML engineering standards (experiment reproducibility, model versioning, CI/CD for ML, automated testing, observability, and incident runbooks) to ensure production-grade delivery.
• Define and implement model monitoring frameworks to detect drift, degradation, and anomalies in production AI/ML systems, with appropriate alerting and retraining triggers.

Nice to have:

• One or more Databricks certifications (e.g., Databricks Certified Machine Learning Professional).
• Experience with vector databases (e.g., Azure AI Search, Pinecone, Weaviate, ChromaDB) and embedding model workflows.
• Familiarity with Power BI semantic models and DAX, and ability to collaborate effectively with BI and data modeling teams on AI-enriched reporting solutions.
• Background in construction, engineering, or infrastructure industry data and business processe