AI Platform Engineer & Architect
Quantum Technologies
Concord, CA, United States
9 days ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Compensation
$147,680.0
Working hours
Regular working hours
Job source
Tech stack
Java (Programming Language)
Application Programming Interfaces (APIs)
Cloud Computing
Software Code Optimization
Continuous Integration
DevOps
Disaster Recovery
Distributed Systems
Fault Tolerance
Fraud Prevention and Detection
Python (Programming Language)
Load Testing
+18 more
Reliability Engineering
Azure Machine Learning
Service Design
Software Deployment
Data Logging
Autoscaling
Istio
Multi-Cloud
Caching
Infrastructure as Code (IaC)
AI Platforms
Kubernetes
Low Latency
Deployment Automation
Machine Learning Operations
Api Design
Dynatrace
Golang
Job description
- 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.
Requirements
- 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.
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