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Kubeflow Mlflow Jobs (NOW HIRING)

Alpha Consulting Corp.

Python + AI

Addison, TX · On-site

$48.75 - $67/hr

... MLFlow • ML deployment using Kubeflow Qualifications : Required : • AI/Gen AI - 60% • Python - 25% • ML - 15% • building LLM-based applications • RAG pipelines • AI APIs using Python ...

Redolent, Inc.

Prinicipal MlOps Engineer

Sunnyvale, CA · On-site

Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX. * Proficiency in monitoring and observability tools like Prometheus, Grafana, ELK Stack, or Datadog ...

Parsons

Technical Software Lead

Fairborn, OH · On-site

$180 - $260/hr

General experience with Artificial Intelligence/Machine Learning technologies, such as Tensorflow, PyTorch, LangChain, vLLM, PyTorch Lightning, SK Learn, LibreChat, Streamlit, KubeFlow, MLFlow ...

New

Parsons

HF Software Engineer

Fairborn, OH · On-site

$110 - $140/hr

General experience with Artificial Intelligence/Machine Learning technologies, such as Tensorflow, PyTorch, LangChain, vLLM, PyTorch Lightning, SK Learn, LibreChat, Streamlit, KubeFlow, MLFlow ...

New

Hirekeyz Inc

MLOps Engineer

San Francisco, CA · On-site

Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI. * Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure)

Allen Control Systems

CV/ML Platform Engineer

Austin, TX · On-site

  • Medical

  • Dental

  • Vision

  • PTO

Experience implementing and maintaining MLOps platforms such as Kubeflow, MLflow, Weights & Biases (W&B), or DVC for experiment tracking and model versioning. * Familiarity with high-performance ...

Deloitte

Senior AI Engineer - SFL Scientific

Los Angeles, CA · On-site

$112K - $154K/yr

Kubernetes, Docker, NVIDIA TensorRT/Triton, RAPIDs, Kubeflow, MLflow, Kafka, etc. • Live within commuting distance to one of Deloitte's consulting offices • Ability to travel 10%, on average ...

TENEX.AI

Senior Data Scientist

San Jose, CA · On-site

... Kubeflow, MLflow, Airflow/Dagster for orchestration). • Solid understanding of probability, statistics, and experimental design. • Experience deploying and maintaining models in a cloud ...

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Kubeflow Mlflow information

What are Kubeflow and MLflow?

Kubeflow and MLflow are open-source platforms designed to simplify and automate machine learning workflows. Kubeflow focuses on running scalable and portable machine learning (ML) workloads on Kubernetes, providing tools for model training, deployment, and management. MLflow, on the other hand, is a platform for managing the ML lifecycle, including experiment tracking, model versioning, and deployment. Both tools can be integrated to streamline developing, tracking, and deploying ML models in production environments.

How do professionals working with Kubeflow and MLflow typically collaborate with data scientists and DevOps teams?

Professionals utilizing Kubeflow and MLflow often serve as a bridge between data scientists, who focus on model development, and DevOps teams, who manage infrastructure and deployment. They facilitate seamless model training, versioning, and deployment pipelines by integrating these tools into the workflow. Collaboration involves regular communication to ensure that models are production-ready, reproducible, and scalable, as well as troubleshooting any pipeline or integration issues that arise. This role requires adaptability and strong teamwork skills to align technical requirements and project goals across departments.

What are the key skills and qualifications needed to thrive as a Kubeflow/MLflow engineer, and why are they important?

To excel as a Kubeflow/Mlflow Engineer, you need a strong background in machine learning lifecycle management, DevOps practices, and cloud-native technologies, typically supported by a degree in computer science or related fields. Hands-on experience with Kubernetes, Docker, Kubeflow, Mlflow, and familiarity with cloud platforms like AWS, GCP, or Azure is highly valuable, along with relevant certifications. Excellent problem-solving, collaboration, and communication skills help you integrate complex workflows and work effectively with data scientists and engineering teams. These capabilities ensure scalable, reliable, and efficient deployment and monitoring of machine learning models in production environments.

What is the difference between Kubeflow Mlflow vs Data Scientist?

AspectKubeflow MlflowData Scientist
Primary FocusMachine learning workflows, deployment, and managementData analysis, modeling, and insights
Required SkillsML Ops, cloud platforms, containerization, PythonStatistics, programming, data visualization
Work EnvironmentCloud-based, DevOps-orientedResearch, analytics, business insights
CertificationsML certifications, cloud certificationsData science, analytics certifications

While Kubeflow Mlflow focuses on managing and deploying machine learning models in production environments, Data Scientists primarily analyze data, build models, and generate insights. Both roles often collaborate but serve different stages of the ML lifecycle.

More about Kubeflow Mlflow jobs

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Kubeflow Mlflow jobs near you
Infographic showing various Kubeflow Mlflow job openings in the United States as of August 2026, with employment types broken down into 33% Temporary, and 67% Contract. Highlights an 100% In-person job distribution.

CV/ML Platform Engineer

Allen Control Systems

Austin, TX • On-site

Full-time

Re-posted 2 days ago

Job description

Job Summary:
Allen Control Systems (ACS) is a cutting-edge defense startup focused on developing autonomous technologies. They are seeking an experienced CV/ML Platform Engineer to design, build, and manage the infrastructure for their Computer Vision and Machine Learning team.
Responsibilities:
• Deploy and operate Kubernetes clusters on bare-metal infrastructure hosting 130+ NVIDIA GPUs, with hybrid burst capability to AWS for scalable compute and storage workloads.
• Manage NVIDIA GPU clusters for ML training.
• Own the ACS CV/ML CI/CD pipeline.
• Improve and maintain core ML infrastructure, such as model registration and versioning, experiment tracking, and model and data provenance tracking.
• Improve and maintain ML model testing, performance analysis, and reporting tools.
• Automate repetitive model training and testing tasks to increase developer velocity.
• Work with Software Team Platform Engineers to ensure efficient coordination and minimal duplication between CV/ML infrastructure and wider Software infrastructure.
• Collaborate with the Software Team to automate the optimization of models (TensorRT/quantization) for deployment on NVIDIA Jetson and other edge hardware.
Qualifications:
Required:
• 2+ years of experience in Platform Engineering or DevOps/MLOps.
• Strong programming skills are required for automating ML lifecycles and building custom CLI tools for CV engineers.
• Hands-on experience with NVIDIA GPU infrastructure, including managing CUDA libraries and development environments, GPU Operator, device plugins, and scheduling (MIG, Volcano, or fractional GPU sharing).
• Experience implementing and maintaining MLOps platforms such as Kubeflow, MLflow, Weights & Biases (W&B), or DVC for experiment tracking and model versioning.
• Familiarity with high-performance storage solutions (e.g., MinIO, WEKA, or Ceph) and data orchestration tools capable of handling terabytes of video/image data.
• Proven track record building CI/CD pipelines that include automated model validation, performance benchmarking, and artifact management for both cloud and edge targets.
• Experience with model optimization toolchains, including TensorRT, ONNX, and quantization techniques, specifically for cross-compilation to ARM targets like NVIDIA Jetson.
• Proficiency with observability stacks (ELK, Prometheus/Grafana) adapted for ML, including monitoring GPU health, training throughput, and model inference metrics.
• Strong Linux systems knowledge (Debian/Ubuntu), including networking for high-throughput data, storage, and security hardening for defense-grade production environments.
Company:
Allen Control Systems develops autonomous defense technologies designed to detect, track, and counter unmanned aerial threats. Founded in 2022, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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