AI Engineer

Responsibilities

• Design, implement, and maintain shared AI platform components such as services, SDKs, templates, workflows, and reusable libraries that software engineering teams can adopt quickly and safely.
• Contribute to engineering patterns and paved paths for AI usage, including model access, prompt handling, evaluation, observability, security, and production support.
• Partner with product, application, data, and platform teams to translate AI use cases into scalable technical solutions instead of one-off implementations.
• Build and operate reliable, cost-aware AI and ML services on cloud platforms using containerized workloads, managed services, and modern infrastructure practices.
• Build and improve internal APIs, developer tooling, and integration patterns that simplify access to AI providers, model endpoints, retrieval services, and supporting data systems.
• Support end-to-end workflows for AI and ML use cases, including data preparation, experimentation, deployment, monitoring, and lifecycle management.
• Contribute to CI/CD practices for AI-enabled services and ML components, including automated quality checks, security controls, and release guardrails.
• Instrument AI workloads with strong observability practices, including metrics, logs, dashboards, tracing, alerting, and cost visibility.
• Troubleshoot and resolve issues related to AI and ML deployments, including latency, scalability, integration failures, reliability problems, and cloud cost concerns.
• Partner with security, platform engineering, and architecture teams to ensure AI usage aligns with company policies for data classification, access control, privacy, and compliance.
• Evaluate emerging AI technologies, frameworks, and vendor capabilities, and share recommendations on where they may fit within ConstructConnect’s engineering roadmap.
• Contribute documentation, runbooks, onboarding materials, and reference implementations that help teams adopt AI capabilities with confidence.

Qualifications

• Required: 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.
• PREFERRED: 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.