AI Platform Engineer & Architect

Summary

Job Title: AI Platform Engineer & Architect
Location: Concord, CA
Duration: 12 Months + Extension
Bill Rate: $71/hour
Job Type: W-2 Contract
Client: To Be Discussed Later
Work Authorization: US-Citizen, H-1B, OPT-EAD, GC-EAD

Key Responsibilities

• Design and implement highly available, low-latency model-serving architectures for real-time inference workloads.

• Develop and standardize deployment patterns for scalable AI/ML services across Kubernetes-based platforms.

• Lead API-based inference service design, integration, deployment, and lifecycle management.

• Optimize model-serving performance through latency tuning, caching strategies, autoscaling, and resource management.

• Establish monitoring, observability, SLOs/SLAs, alerting, and operational runbooks for production services.

• Drive load testing, capacity planning, resiliency engineering, and disaster recovery readiness.

• Integrate inference platforms with CI/CD pipelines to enable automated deployments and controlled releases.

• Collaborate with Data Science, Platform Engineering, MLOps, and Infrastructure teams to ensure reliable production model operations.

• Establish operational best practices, security standards, reliability measures, and performance benchmarks for enterprise AI deployments.

• Strong experience with online inference and production model-serving architectures.

• Expertise in real-time APIs and distributed systems design.

• Hands-on experience with Kubernetes, container orchestration, and service mesh technologies.

• Knowledge of autoscaling, capacity management, and workload optimization.

• Experience in performance engineering, load testing, and latency optimization.

• Strong understanding of monitoring, observability, logging, distributed tracing, and SLO management.

• Experience with CI/CD, DevOps, and Infrastructure as Code (IaC).

• Knowledge of reliability engineering, fault tolerance, and resiliency patterns.

• Experience operating cloud-based and on-premises platforms.

• Proficiency in Python, Java, Go, or similar backend programming languages.

• Experience with MLOps platforms and enterprise AI deployment frameworks.

• Hands-on experience with real-time recommendation, fraud detection, risk assessment, personalization, or predictive analytics platforms.

• Familiarity with GPU-based inference, model optimization, and multi-cloud deployments.