OverviewThis 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 Opportunity 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 ConstructConnect'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.
ResponsibilitiesWhat You'll Be Doing- 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., Google Cloud Platform) 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.
QualificationsWhat You Bring to the Team- Strong software engineering background with proficiency in at least one modern programming language (such as Python, Go, or TypeScript) and solid software design and debugging skills.
- Experience building and operating services on a major cloud platform (such as Google Cloud Platform), including familiarity with managed compute, storage, and networking services.
- Hands-on experience with container technologies such as Docker and Kubernetes, including deploying, scaling, and monitoring containerized workloads.
- Proficiency with CI/CD pipelines (e.g., Git-based workflows) to automate building, testing, and deploying services and ML components.
- Experience with infrastructure-as-code tools such as Terraform to provision and manage cloud resources in a repeatable, auditable way.
- Familiarity with MLOps concepts and tools (such as Kubeflow, TFX, MLflow, or Vertex AI) for managing the ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
- Understanding of modern AI capabilities (e.g., generative AI, embeddings, basic NLP and computer vision concepts) and how they can be safely integrated into products and engineering workflows.
- Experience building APIs, services, or libraries that are used by other engineers, with a strong focus on usability, stability, and documentation.
- Proficiency in using version control systems like Git for tracking changes in infrastructure, code, and configuration.
- Strong foundation in observability practices (metrics, logs, dashboards, alerting) and experience using them to manage reliability and performance.
- Effective communication skills and experience working cross functionally with product, data, infrastructure, and security teams.
- Demonstrated ability to translate complex technical topics into clear, action-oriented guidance for other engineers.
- Team player with the ability to balance multiple simultaneous projects and collaborate in a distributed, remote friendly environment.
- Bachelor's degree or equivalent experience in Computer Science, Software Engineering, Data Science, or a related field.
Physical Demands and Work Environment - The physical activities of this position include frequent sitting, telephone communication, working on a computer for extended periods of time. Visual acuity is required to perform activities close to the eyes.
- Team members are expected to maintain a dedicated and ergonomically appropriate remote workspace.
- Team members who live within commuting distance of our office location (Atlanta, Georgia) are expected to work in a hybrid capacity, with regular in-office presence every Tuesday and Wednesday each week.
- All team members must reside and perform their work within the United States
E-Verify Statement ConstructConnect utilizes the E-Verify program with every potential new hire. This makes it possible for us to make certain that every employee who works for ConstructConnect is eligible to work in the United States. To learn more about E-Verify you can call 1- or visit their website. E-Verify is a registered trademark of the United States Department of Homeland Security.
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