AI Platform Engineer & Architect

Quantum Technologies. LLC; Concord (CA); On-site; USD 81,000 - 115,000; Full time

Job summary

Quantum Technologies LLC in Concord, CA is seeking an AI Platform Engineer & Architect for a 12-month + extension W-2 contract. The role centers on designing high-availability model-serving architectures for real-time inference.

You will standardize deployment patterns on Kubernetes, lead API-based inference service design, and integrate platforms with CI/CD pipelines for controlled releases. The position requires strong skills in distributed systems, latency optimization, observability, and

Qualifications

• 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.

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.

Skills

Online inference

Real-time APIs

Kubernetes

Container orchestration

Service mesh

Autoscaling

Latency optimization

CI/CD

IaC

Python

Java

Go

Distributed systems

Tools

Kubernetes

Job description

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.

Required Technical Skills

• 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.

Preferred Qualifications

• 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.

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.