Machine Learning Operations Engineer — Dallas, TX

About Us

With b5MxDp, you can advance beyond AI trained on public knowledge to custom AI that actually understands how your unique business operates. The platform continually applies evolving enterprise knowledge and operational context, reasoning across relationships, dependencies and changing business conditions. With b5MxDp, you can deploy AI that stays aligned with missioncritical operations, supports strategic decisions and modernizes business workflows while continuously improving over time.

About This Role

As Machine Learning Ops Engineer at b5MxDp, 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 workloads

• Manage GPU clusters and deploy models using Kubernetes and Docker to ensure scalable, efficient 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 all 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 or equivalent technology

• 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

Compensation

• $140,000 – $225,000 per year

• Base salary as listed per role, plus equity package

• Performance-based commission structure for quota-carrying positions

• Annual merit-based performance reviews targeting 20% of base salary for non-quota-carrying roles

Benefits

• Comprehensive healthcare coverage (80% employer-paid)

• 401(k) with company matching

• Flexible PTO starting at 15 days

• 11 paid holidays

Professional Development

• Annual training and certification budget

• Access to enterprise learning platforms

• Conference attendance opportunities

• Internal workshops and knowledge-sharing sessions

Work Environment

• On-site at our Dallas, TX office, 5 days per week

• Standing desks and modern collaborative workspace

• Monthly team events

• Company-provided laptop

Interview Process

• Recruiter interview (30–60 minutes)

• Hiring manager interview (30–60 minutes)

• Technical interview loop (half or full day)

• CEO interview (30–60 minutes)

Application Materials

Hit that apply button:

• Git Hub profile or code samples

• Portfolio of relevant work describing your development experience

9ZDgM6yD is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees.