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 andAI 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) 4MindsAI is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. #J-18808-Ljbffr