Machine Learning Engineer II

Overview

This position is hybrid in Peachtree Corners, Georgia and sits within our Product Development division, which develops, tests, and improves our software solutions in an innovative and collaborative environment.

The construction industry is ready for innovation. Our software engineering teams are rapidly adopting AI using generative models, intelligent search, and ML-powered services to help customers work smarter and to help our own engineers move faster. The AI Acceleration & Enablement team is driving this transformation by providing safe, scalable paths for AI adoption across the SDLC, from coding assistants to production AI services.

The ideal candidate for this role will have a passion for building platforms that enable others. You bring a strong engineering background (cloud, infrastructure, CI/CD, MLOps) and are excited to create “paved paths” for software engineers to use AI tools, models, and patterns safely and reliably. Come help us accelerate AI adoption across Construct Connect’s engineering organization.

As a Machine Learning Engineer II (AI Enablement), you will play a crucial role in designing, implementing, and operating the shared AI/ML platform capabilities that other engineers build on. You will work across infrastructure, data, and application teams to turn AI experiments into robust, repeatable, and observable solutions. You will help standardize how we integrate AI into products and into engineers’ day-to-day workflows.

• Design, implement, and maintain shared AI/ML platform components (services, SDKs, templates) that software engineering teams can easily adopt.
• Be a leading member of the AI Acceleration & Enablement team to define “golden paths” for AI usage (e.g., how services call LLMs, how we handle prompts, logging, and safety, how we monitor AI features).
• Partner with product and application teams to understand their AI use cases and translate them into scalable, secure platform capabilities instead of one-off solutions.
• Work closely with data engineers and ML practitioners to design and deploy end-to-end pipelines that prepare data, train/fine-tune models, and deploy them into production.
• Build and operate reliable, cost‑aware AI/ML services on cloud platforms (e.g., GCP) using containerized workloads (Docker, Kubernetes) and managed services.
• Implement and maintain CI/CD pipelines to automate testing, building, and deploying AI/ML components, including automated checks and guardrails for quality and security.
• Develop and support internal libraries, command-line tools, and APIs that simplify integration with AI providers, model endpoints, feature stores, and data services.
• Instrument AI and ML workloads with robust observability—metrics, logs, dashboards, and alerts—for performance, reliability, and cost.
• Troubleshoot and resolve issues related to AI/ML deployments, scalability, latency, cost, and integration with upstream/downstream systems.
• Partner with security and platform engineering to ensure AI usage follows company policies for data classification, access control, and compliance.
• Stay informed about industry trends, best practices, and emerging technologies in AI, MLOps, and developer productivity, and help evaluate where they fit into our roadmap.
• Mentor and guide engineers on best practices for using AI tools (e.g., coding assistants, chat-based AI) and integrating AI into their services.
• Conduct thorough code reviews to ensure code quality, maintainability, and adherence to platform and security standards.
• Contribute documentation, runbooks, and onboarding materials that help new teams quickly and safely adopt AI on the platform.
• Participate in the recruiting and onboarding of new team members.
• This job description in no way implies that the duties listed here are the only ones that team members can be required to perform.

• Strong software engineering background with proficiency in at least one modern programming language (such as Python, Go, or Type Script) and solid software design and debugging skills.
• Experience building and operating services on a major cloud platform (such as GCP),…