AI Engineer – Fulltime Full Time NEW

Corporate AI Engineer

Role Summary/Projects:

The first is at the corporate level, where they are building an AI assistant that will function as a companywide knowledge base. The goal is to help junior employees quickly find answers to routine questions, reducing the need to rely on managers for basic information.

The second is at the construction site level, where they are applying AI to improve document retrieval, RFI workflows, and billing and transaction processes. These initiatives are designed to increase efficiency across their job sites and streamline day to day construction operations.

Responsibilities

• Enterprise Workflow Analysis: Collaborate with corporate functions (Finance, HR, Operations, Safety) and project leaders to identify pain points and AI opportunities that can be standardized across the company.
• AI Agent Development: Build and deploy multiple production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or Python-based frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
• RAG Pipelines & LLMOps: Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift across sites.
• Cross-Cloud Engineering: Implement and maintain integrations across OpenAI, Azure OpenAI, and AWS Bedrock services with secure custom connectors.
• Data Integration: Partner with Data Engineering to deliver ETL/ELT pipelines, APIs, and event-driven connectors that enable enterprise-wide AI solutions.
• Adoption & Change Enablement: Support onboarding and training for both corporate users and field teams, track adoption metrics, and iterate solutions for stronger business impact.
• Documentation & Communication: Produce clear technical documentation, user stories, and specs for AI solutions, while translating outcomes into business value for corporate leadership.
• Governance & Compliance: Ensure all AI solutions meet the company’s data governance, security, and compliance requirements.

Qualifications

•    5+ years of Software Development experience.

•    2+ years in AI or ML-focused engineering, including hands-on experience with LLMs and RAG Pipelines.

•    Proven experience building multiple AI agents in production environments.

•    Must have strong traditional coding background in Python.

•    Excellent communication and stakeholder management skills.

Bonus Points

•    Experience with AWS Bedrock.

•    Experience with SQL and Databricks.

•    Experience building multi-agent frameworks.