The MLOps Engineer will design scalable ML infrastructure, manage containerized environments, develop training pipelines, and implement CI/CD pipelines, ensuring model reliability and performance.
Location: Fully Remote
Preferred Locations: Midwest-based; Indianapolis, IN or IL, Chicagoland Area preferred
Who We Are
We are K1X. Our platform powers a modern, all-digital K-1 experience by replacing legacy workflows with scalable software and AI-driven automation.
As we expand our machine learning capabilities, we are investing in a robust ML platform that enables production-grade model development, deployment, and monitoring across our products.
About your Role
We’re seeking an experienced Machine Learning Operations (MLOps) Engineer to join our team and build the infrastructure that powers AI and machine learning at K1X.
This is a hands-on role focused on designing scalable systems, pipelines, and tooling that enable our Machine Learning Engineers to efficiently train, deploy, and operate models in production.
You’ll work at the intersection of software engineering, DevOps, and machine learning—owning the reliability, scalability, and performance of our ML platform.
Your Responsibilities
- Design and build scalable ML infrastructure to support model training, evaluation, and deployment.
- Develop and maintain containerized environments using Docker and Kubernetes.
- Build and manage distributed training pipelines and orchestration workflows.
- Implement and maintain ML lifecycle tooling such as MLflow for experiment tracking and reproducibility.
- Own production inference systems, including NVIDIA Triton Inference Server.
- Design and operate low-latency, high-availability model serving architectures.
- Implement CI/CD pipelines for ML deployment, versioning, and rollback strategies.
- Build and maintain data pipelines integrated with Snowflake and related data systems.
- Implement monitoring, logging, and alerting for model performance, drift detection, and system health.
- Partner with ML Engineers to improve developer experience and accelerate delivery.
Requirements
Who You Are:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent experience.
- 5+ years of experience in software engineering, DevOps, or MLOps roles.
- Strong proficiency in Python and experience building production-grade systems.
- Hands-on experience with Docker, Kubernetes, and distributed systems.
- Experience building and maintaining CI/CD pipelines.
- Familiarity with ML lifecycle tools such as MLflow or similar.
- Experience working with cloud-based data platforms such as Snowflake.
- Strong understanding of system design, APIs, and microservices architectures.
- Proven debugging and troubleshooting ability across distributed systems.
It's Truly a Match If You Have:
- Experience managing inference infrastructure such as NVIDIA Triton Inference Server.
- Experience building large-scale training infrastructure including GPU workloads and distributed training.
- Familiarity with feature stores, data versioning, and experiment tracking systems.
- Experience supporting NLP or document processing pipelines.
- Exposure to observability tools such as Prometheus, Grafana, or similar.
- Experience working in SaaS environments with high availability, productivity, and performance requirements.
- A strong bias toward automation, scalability, and continuous improvement.
- A collaborative mindset and ability to work cross-functionally with engineering and data teams.
Benefits
- Unlimited Vacation Policy + Sick Time
- Fully Remote Opportunity
- Benefits/401K
- Growing Startup Culture
- Unlimited Vacation Policy + Sick Time + Holidays
- Paid Parental Leave
- Fully Remote Opportunity
- Healthcare Benefits and 401K
- Growing Startup Culture
Top Skills
Docker
Kubernetes
Mlflow
Nvidia Triton Inference Server
Python
Snowflake
Similar Jobs
Artificial Intelligence • Machine Learning • Natural Language Processing • Software • Conversational AI
As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.
Top Skills:
Ci/CdDatadogDockerGrafanaKubernetesNvidia Triton Inference ServerOnnx RuntimePrometheusPulumiPythonTensorrtTerraform
Information Technology • Software
Lead the design and scaling of ML infrastructure, work on model lifecycle, optimize workflows, and mentor engineers in a fast-paced AI environment.
Top Skills:
AWSAzureCudaDockerGCPGoHelmKubernetesLlama-FactoryMegatron-LmPythonPyTorchTransformersTypescriptVllm
Artificial Intelligence • eCommerce • Software
As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.
Top Skills:
AWSAzureCi/CdGCPPythonPyTorchTensorFlow
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
