AI Platform Engineer

Location: North Brunswick
Category: IT Infrastructure
Employment Type: Contract
Work Location: Hybrid
Job ID: 35820
Date Added: 08/21/2025

Platform Engineer – 3 days onsite in Irving, Phoenix, NJ or Charlotte

Mainz Brady Group is seeking an AI Platform Engineer with strong experience in supporting and automating AI/GenAI workloads in production. This role involves managing data pipelines, observability, and deployment of predictive AI systems across cloud environments.

AI Platform experience 
    • Hands-on experience with vector databases like Elasticsearch or Similar
    • Understanding of similarity search, indexing strategies, and embedding management
    • Understanding the enterprise DR solutions with backup and restore hands on experience 
Linux System Proficiency
    • Strong command-line skills for debugging, automation, and deployment
    • Experience with shell scripting and system-level monitoring
Python programming
    • Proficient and hands on experience with building an  automation scripts
    • Knowledge about Python for building AI models, data pipelines, and OpenAI
Production Support
    • Experience supporting predictive AI workloads in production environments
    • Ability to troubleshoot complex issues across data ingestion, model inference, and deployment layers
    • Familiarity with CI/CD pipelines and containerization (Docker, Kubernetes)
    • Able to provide the support for existing predictive and GenAI data pipelines as per the roster ( 1 Week support expected for every 6 to 8 weeks)
Observability & Monitoring
  • Ability to define and implement observability strategies for AI systems
  • Experience with tools like Splunk,  Grafana, ELK stack, or OpenTelemetry
  • Proactive monitoring of model failures, latency, and system health
Data Science Lifecycle Experience (Bonus)
  • Multi cloud experience (GCP and Azure)
  • Exposure to full-cycle data science projects: problem definition, data exploration, modeling, evaluation, and Training, scoring, operationalize the models
  • Understanding of MLOps principles and model lifecycle management
  • Collaboration with data scientists to understand the use cases and help them to deploy solutions 



 

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