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.
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Required Technical SkillsStrong 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.
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Preferred QualificationsExperience 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.
Equal Opportunity Employer:
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law
