AI Engineer
HybridAtlanta, Georgia, United States
Job Summary
Design, implement, and maintain shared AI platform components including services, SDKs, templates, and reusable libraries for internal engineering teams. Build and operate reliable, cost-aware AI and ML services on cloud platforms using containerized workloads and modern infrastructure practices. Partner with product, data, and security teams to translate AI use cases into scalable technical solutions while contributing to CI/CD practices, automated quality checks, and release guardrails. Instrument workloads with observability practices covering metrics, logs, dashboards, and cost visibility. Troubleshoot deployment issues related to latency, scalability, and reliability, ensuring alignment with company policies for data classification and privacy.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience
- 5–7 years of experience in software engineering, machine learning engineering, platform engineering, or a related area building and operating production systems
- Strong proficiency in at least one modern programming language such as Python, Go, or TypeScript, along with solid software design, debugging, and engineering fundamentals
- Experience building and operating services on a major cloud platform, preferably Google Cloud Platform, including familiarity with compute, storage, networking, and managed services
- Hands-on experience with containers and orchestration technologies such as Docker and Kubernetes
- Experience with CI/CD pipelines and Git-based engineering workflows used to build, test, and deploy services and platform components
- Familiarity with infrastructure-as-code tools such as Terraform for provisioning and managing cloud resources in a repeatable, auditable way
- Familiarity with MLOps concepts and tools used to support model training, evaluation, deployment, and monitoring
- Understanding of modern AI capabilities such as generative AI, embeddings, retrieval patterns, NLP, and related ML concepts, and the ability to apply them responsibly in production environments
- Experience building APIs, services, platforms, or libraries that are consumed by other engineers, with a focus on reliability, usability, and documentation
- Strong foundation in observability and operational excellence, including experience managing service health through metrics, logs, dashboards, and alerting
- Experience working cross-functionally with product, data, infrastructure, platform, and security teams
- Ability to translate technical topics into practical guidance and collaborate effectively in a distributed, remote-friendly environment
- Team members are expected to maintain a dedicated and ergonomically appropriate remote workspace
- Team members who live within commuting distance of one of our office locations (Greater Cincinnati/Northern Kentucky or Atlanta, Georgia) are expected to work in a hybrid capacity, with regular in-office presence as determined by the team or department
- All team members must reside and perform their work within the United States
- ConstructConnect utilizes the E-Verify program with every potential new hire
Desired Qualifications
- Experience supporting shared AI enablement, developer productivity, or platform engineering initiatives in a multi-team SaaS environment
- Experience with Google Cloud AI and data services such as Vertex AI, BigQuery, or related managed tooling
- Familiarity with AI evaluation frameworks, guardrails, prompt management, model routing, and lifecycle governance
- Experience working with vector search, retrieval-augmented generation, agentic workflows, or orchestration frameworks in production settings
- Exposure to intelligent search, recommendation systems, NLP, or computer vision use cases
- Background working in environments where security, governance, and operational reliability are important to AI adoption
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