AI Platform Engineer & Architect

Valiant Technologies LLC; Concord (CA); Presencial; USD 83.000 - 113.000; Jornada completa

Descripción de la vacante

Valiant Technologies LLC seeks an AI Platform Engineer & Architect to design scalable model-serving systems in Concord, CA. The role focuses on building low-latency real-time inference, deploying AI/ML services across Kubernetes, and owning API-based inference lifecycle from design to production.

You will optimize performance, implement autoscaling, monitoring, and resiliency, and drive CI/CD integrations. Collaboration with Data Science, MLOps, and Infra teams is essential for enterprise-grade

Formación

• Experience with online inference serving architectures.
• Real-time APIs and distributed systems design experience.
• Kubernetes-based platforms, container orchestration, service mesh exposure.
• Knowledge of autoscaling, capacity management, and workload optimization.
• Experience with performance engineering, load testing, and latency optimization.
• Strong monitoring, observability, logging, tracing, and SLO management.
• Experience with CI/CD, DevOps, and Infrastructure as Code (IaC).
• Knowledge of reliability engineering, fault tolerance, and resiliency patterns.

Responsabilidades

• 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 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 runbooks for production services.
• Drive load testing, capacity planning, resiliency engineering, and disaster recovery readiness.
• Integrate inference platforms with CI/CD pipelines for 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.

Conocimientos

Online inference

Real-time APIs

Distributed systems

Kubernetes

Autoscaling

Performance engineering

Monitoring & observability

CI/CD & IaC

Cloud & on-prem

Backend programming (Python/Java/Go)

Herramientas

Kubernetes

Container orchestration

Service mesh

CI/CD tooling

IaC (Terraform/CloudFormation)

Descripción del empleo

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

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.