AI Platform Architect AI Platform & Infrastructure

Location: New Jersey / New York
Work Schedule: Hybrid 4 days per week onsite

We are seeking an experienced AI Platform Architect to design, build, and govern scalable, secure, cloud-native AI platforms at enterprise scale. This is a hands-on technical role requiring strong Python engineering skills combined with deep expertise in cloud architecture, AI/ML infrastructure, and platform engineering.

The AI Platform Architect will play a key role in enabling reliable, high-performance AI systems by defining platform standards, guiding implementation, and partnering closely with engineering and MLOps teams.

• Design and evolve cloud-native AI platform architectures across enterprise environments

• Lead Python-based platform and service development, including APIs, orchestration layers, and model-serving systems

• Architect and implement AI platforms using managed cloud services (compute, storage, networking, identity)

• Define standards for model deployment, monitoring, versioning, and lifecycle management

• Ensure platform security, performance, reliability, cost efficiency, and regulatory compliance

• Collaborate with engineering and MLOps teams on cloud infrastructure, CI/CD pipelines, and automation

• Review and guide Python codebases to ensure quality, scalability, and maintainability

• Evaluate and select cloud services, AI frameworks, and platform tooling

• Support long-term platform scalability planning, architecture evolution, and technical roadmaps

• 8+ years of experience in platform engineering, cloud architecture, or systems architecture

• Strong, hands-on experience with Python, building production-grade services and frameworks

• Deep expertise designing and operating cloud platforms (Azure, AWS, or Google Cloud Platform)

• Experience with cloud-native architectures, including containers, orchestration, event-driven systems, and APIs

• Strong understanding of AI/ML infrastructure, MLOps practices, and distributed systems

• Hands-on experience implementing CI/CD pipelines, infrastructure-as-code, and automated deployments

• Ability to translate architectural designs into executable Python and cloud implementations

• Strong problem-solving skills and ability to work across technical and business teams

• Experience in regulated or large-scale enterprise environments preferred

• Experience with Kubernetes, Docker, and container orchestration platforms

• Exposure to model serving frameworks and observability tools

• Familiarity with security, compliance, and governance frameworks

• Experience supporting enterprise AI platforms at scale

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