Artificial Intelligence Engineer

Principal Architect - Lead, AI Platform Engineering Location: Remote (Maryland preferred) Reports To: Director, Enterprise AI 1-2 year Contract-to-Full-time About the Role We are seeking an accomplished Lead, AI Platform Engineering professional to drive the engineering backbone of our enterprise AI and machine learning ecosystem. In this senior technical role, you will architect, build, and optimize the core infrastructure, tooling, and reusable components that enable scalable, secure, and high-performing AI solutions across the organization. You will collaborate across data engineering, cloud infrastructure, MLOps, and product teams to deliver modern platform capabilities spanning compute, data, pipelines, observability, and developer experience. This position is ideal for a hands-on engineer who thrives in solving complex system challenges and setting technical direction. What You’ll Do AI Infrastructure & Platform Engineering • Lead the architecture and operation of cloud and on‑prem AI infrastructure, including compute orchestration, GPU optimization, and scalable model hosting. • Integrate the AI platform with enterprise data pipelines, feature stores, and data platforms to ensure reliable and performant model inputs. • Partner with networking, cloud, and security teams to ensure performance, resilience, and compliance across the platform. MLOps & Operational Foundations • Build and advance end‑to‑end MLOps capabilities such as model versioning, approval workflows, automated testing, and deployment automation. • Own and enhance the enterprise model registry, ensuring strong metadata, lineage, governance, and lifecycle practices. • Develop CI/CD pipelines optimized for ML lifecycle workflows (training, tuning, packaging, deployment). • Implement observability frameworks, including logging, monitoring, tracing, drift detection, and performance dashboards. Tooling, Frameworks & Developer Experience • Develop reusable components, SDKs, templates, and internal tools to accelerate model development and standardization. • Define and maintain reference architectures for AI services, integration patterns, and pipelines. • Drive continuous improvements in developer experience to support experimentation, deployment efficiency, and operational excellence. • Evaluate emerging technologies and introduce new approaches that advance the platform’s capabilities. Cross-Functional Technical Leadership • Collaborate closely with data science, data engineering, and product teams to translate needs into scalable, maintainable solutions. • Provide mentorship and technical guidance to engineers across platform and AI infrastructure initiatives. • Communicate platform strategies, standards, and roadmaps to technical stakeholders. • Advocate for best practices in reliability, security, automation, and performance optimization. Required Qualifications • 4-6+ years in software engineering, cloud infrastructure, data platforms, or MLOps engineering. • Demonstrated experience designing and operating cloud‑based AI/ML infrastructure (AWS, Azure, GCP, or hybrid). • Hands-on experience with MLOps practices such as CI/CD, model registries, orchestration frameworks, and production monitoring. • Strong expertise in Kubernetes, containerization, distributed compute, and GPU orchestration. • Proven ability to lead technical initiatives and deliver complex platform capabilities in an agile environment. Preferred Qualifications • Experience developing internal platforms, SDKs, or reusable developer tooling. • Familiarity with feature stores, vector databases, or real‑time data services. • Background in high‑scale or regulated industries. • Proficiency with infrastructure-as-code, DevOps automation, and observability tooling. Why Join Us? • Influence the architecture and strategy of enterprise-wide AI systems. • Work with modern technologies and shape platform standards used across teams. • Collaborate with a highly skilled technical organization focused on innovation and impact. • Opportunity to drive meaningful change in how AI is built, deployed, and scaled across the enterprise.