Machine Learning Operations Engineer

4MindsAI Inc.•Dallas, TX

2d•$130,000 - $200,000•Onsite

About The Position

4Minds is an enterprise AI fine-tuning platform that transforms how organizations build and operate private, domain-specific AI. Unlike static systems, 4Minds’s AI platform learns continuously from live data in real time and can be deployed on-prem or your cloud provider. Our patented technologies scale existing engineering teams and empower new AI teams, enabling rapid AI deployment, adaptation, and ROI. Through 4Minds’s automated data pipeline and proprietary knowledge graph, enterprises can connect all their data sources, including Microsoft, Databricks, AWS and Google, creating adaptive AI that surpasses the capabilities of conventional RAG-based systems. As Machine Learning Ops Engineer at 4Minds, you will own the infrastructure that makes our AI platform perform, scale, and ship across the most demanding deployment environments in the enterprise market: GCP, AWS, Azure, CoreWeave, and on-premise. This isn't a role where you maintain what others built. You'll actively research, evaluate, and drive improvements across every layer of the stack, from inference pipeline reliability to GPU performance optimization across hardware architectures. Working in close partnership with the CTO, you'll take on initiatives that sit at the frontier of what's possible with modern AI infrastructure. Our platform's ability to deploy privately, on-premise or in any cloud, is a core product promise, and you're the engineer who makes that promise real at scale. This is a senior, hands-on role on a focused engineering and research team. You'll bring production discipline to a system that demands it, while continuously pushing the boundaries of how we scale, optimize, and extend our infrastructure as the platform grows.

Requirements

5+ years of hands-on experience in production ML infrastructure engineering, with a track record of deploying and operating AI models at scale
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Deep proficiency with Kubernetes and Docker for deploying and managing AI workloads across diverse environments
Hands-on experience with CI/CD pipelines designed for AI and ML model lifecycle management
Experience designing and managing infrastructure across multiple cloud platforms, including at least two of: GCP, AWS, Azure, CoreWeave
Solid understanding of GPU cluster management and the performance tradeoffs across hardware configurations
Experience with on-premise AI deployment and the infrastructure complexity it introduces
Strong Grasp of MLOps principles and AI model lifecycle management from experimentation through production
Ability to work autonomously, make infrastructure decisions with limited oversight, and communicate technical tradeoffs clearly to senior leadership

Nice To Haves

7+ years of ML infrastructure experience, with increasing ownership of complex, multi-environment deployments
Familiarity with GPU scaling research across hardware architectures beyond Nvidia
Background working directly with research or data science teams to productionize experimental models
Experience in high-growth startups or early-stage companies where infrastructure ownership is broad and fast-moving
Familiarity with real-time performance monitoring and observability tooling for AI systems
Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Responsibilities

Design, build, and continuously improve CI/CD pipelines that move AI models reliably from development through production, including testing, validation, and deployment automation
Own inference pipeline reliability and performance across GCP, AWS, Azure, CoreWeave, and on-premise environments, proactively identifying and implementing improvements
Research and evaluate GPU scaling approaches across hardware architectures to inform infrastructure decisions and extend platform capabilities
Implement and manage Nvidia Triton Inference Server and leverage Nvidia Fleet Command to streamline model inference workflows
Manage GPU clusters and deploy models using Kubernetes and Docker to ensure scalable, efficient model serving across all deployment environments
Automate model retraining and redeployment processes in response to data updates and performance changes
Monitor system health, performance, and reliability using AI observability tools, with a focus on continuous improvement rather than maintenance alone
Partner closely with the CTO on infrastructure research initiatives, translating emerging hardware and deployment capabilities into production-ready systems
Support early on-premise customer installations and contribute to knowledge transfer as Solutions Engineering takes ownership of that function