[Remote] Lead AI Engineer

Note: The job is a remote job and is open to candidates in USA. Dice is a leading technology company specializing in AI solutions, and they are seeking a Lead AI Engineer to define and maintain AI/ML architecture on their Databricks Lakehouse. The role involves overseeing the delivery of AI/ML use cases, providing technical leadership, and ensuring compliance with engineering standards.

Responsibilities

• 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
• 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

Skills

• 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
• 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
• 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 processes

Company Overview

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