Machine Learning Operations Engineer

Mission

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.

Role Overview

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.

Key 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
• Required Qualifications
• 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
• Strong working knowledge of Nvidia Triton Inference Server and TensorRT
• 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

Preferred Qualifications

• 7+ years of ML infrastructure experience, with increasing ownership of complex, multi-environment deployments
• Experience with Nvidia Fleet Command for managing distributed inference 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
• If you're passionate about building the infrastructure that powers private, continuously-learning AI for the world's most demanding enterprises, we'd love for you to apply and help shape the foundation that makes custom AI a reality at scale.

Compensation

Base salary range: $130,000 - $200,000 annually

Competitive equity package in venture-backed startup

Performance-based bonus structure

Annual merit-based salary reviews

Stock

Benefits

Comprehensive medical, dental, and vision coverage (80% employer-paid)

401(k) plan with company match

Unlimited PTO policy with 15 days minimum

11 paid company holidays

Professional Development

Annual training and certification budget

Access to online learning platforms

Conference attendance opportunities

Regular internal technical workshops and knowledge sharing sessions

Work Environment

Onsite at Dallas HQ

High-performance workstations

Modern office space in Dallas with standing desks and ergonomics equipment

Monthly team events and learning sessions

Collaborative in-office environment fostering innovation and teamwork

Process

We like to be efficient but do our due diligence. Here’s what you’ll expect from us:

Interview with Recruiter (30-60 minutes)

Interview with Hiring Manager (30 minutes)

Technical Interviews and/or Presentation (half to full day)

Interview with CEO (30 minutes)

Apply Now:

Hit that apply button:

Detailed Resume

Git Hub profile or code samples

Portfolio of relevant work

Brief cover letter describing your development experience

4MindsAI is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees.