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Remote Kubeflow Jobs (NOW HIRING)

Frontier Technology Inc.

Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference ... Remote

Inizio Partners

Machine Learning Engineer (GCP)

Manhattan, NY · Remote

$58.25 - $79.75/hr

Location- Remote Overview: As a GCP ML Engineer, you'll design, develop, and maintain machine ... Hands-on experience with MLFlow or Kubeflow. * Familiarity with data engineering processes, ETL ...

Qualysoft

Remote bevorzugt, gelegentliche Vor-Ort-Termine nach Absprache 400 - 450 a day Aufgaben Aufbau und ... Kubeflow Pipelines) Integration von ML-Tracking- und Experiment-Tools (MLflow, TensorBoard, o. A ...

Momento USA

AI Platform Lead Engineer @ Remote

Belmar, NJ · Remote

$104K - $138K/yr

Remote We are seeking a seasoned AI Platform Engineer Lead with over 14 years of experience in ... Strong knowledge of MLOps frameworks (Kubeflow, MLflow, SageMaker, Vertex AI). * Hands-on ...

Berriehill Research

Senior AI Systems Engineer

Albuquerque, NM · On-site +1

$95K - $130K/yr

... such as MLflow, Kubeflow, vLLM, or similar. * Experience with simulations for scientific or ... This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be ...

Berriehill Research

Senior AI Systems Engineer

Raleigh, NC · On-site +1

$92K - $126K/yr

... such as MLflow, Kubeflow, vLLM, or similar. * Experience with simulations for scientific or ... This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be ...

Azumo

Data Scientist, Applied AI - Remote

The position is FULLY REMOTE , based in Latin America. Professional English proficiency (B2/C1 ... Exposure to MLOps/LLMOps tools ( MLflow , Kubeflow , TFX ). * Experience with Large Language Models ...

Moody's Analytics

MLOps Engineer

New York, NY · On-site +1

We have a flexible work environment and allow remote work depending on one's personal choice ... Familiarity with MLflow (or similar platforms like Kubeflow and other tools) * Promotes a practice ...

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Remote Kubeflow information

What is the difference between Remote Kubeflow vs Remote Data Scientist?

AspectRemote KubeflowRemote Data Scientist
Required CredentialsCloud certifications, Kubernetes, ML OpsStatistics, Machine Learning, Programming
Work EnvironmentCloud platforms, DevOps toolsData analysis, modeling, research
Industry UsageAI/ML deployment, MLOps teamsData analysis, predictive modeling

Remote Kubeflow focuses on deploying and managing ML workflows using Kubernetes, requiring cloud and DevOps skills. Remote Data Scientists analyze data, build models, and interpret results. While both roles involve machine learning, Remote Kubeflow emphasizes deployment and infrastructure, whereas Remote Data Scientists focus on data analysis and modeling.

What are the key skills and qualifications needed to thrive as a Remote Kubeflow Engineer, and why are they important?

To thrive as a Remote Kubeflow Engineer, you need strong expertise in machine learning, cloud computing, and container orchestration, typically supported by a degree in computer science or related fields. Proficiency with tools such as Kubeflow, Kubernetes, Docker, and cloud platforms like AWS, GCP, or Azure—as well as experience with CI/CD pipelines—is essential. Strong problem-solving skills, communication, and the ability to collaborate remotely are important soft skills for success. These skills ensure the effective deployment and management of scalable machine learning workflows in distributed, cloud-based environments.

What are some common challenges faced by professionals working in a remote Kubeflow engineer role?

Remote Kubeflow engineers often encounter challenges such as troubleshooting distributed machine learning pipelines without direct, on-premises access to infrastructure. Effective communication with data scientists, DevOps, and other stakeholders can also be more complex due to differing time zones and remote collaboration tools. Additionally, managing secure access and ensuring seamless deployment of ML workflows in cloud environments requires a strong understanding of both Kubernetes and Kubeflow. Overcoming these challenges typically involves proactive documentation, regular virtual meetings, and a collaborative approach to problem-solving.

What is a Remote Kubeflow job?

A Remote Kubeflow job refers to a role where professionals use Kubeflow, an open-source machine learning platform designed for Kubernetes, while working remotely. These jobs typically involve designing, deploying, and managing machine learning workflows on cloud or on-premises Kubernetes clusters. Responsibilities may include automating ML pipelines, optimizing model training, and collaborating with data scientists and engineers. Remote Kubeflow professionals usually need expertise in Kubernetes, Docker, Python, and machine learning concepts. The remote aspect allows them to perform these tasks from anywhere with reliable internet access.
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What are the most commonly searched types of Kubeflow jobs? The most popular types of Kubeflow jobs are:
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Remote Kubeflow jobs near you
AI/ML Engineer

AI/ML Engineer

Frontier Technology Inc.

Huntsville, AL • Remote

Full-time

Posted 10 days ago

Job description

Overview

FTI Defense is seeking a hands-on AI/ML Engineer to design, build, and deploy advanced machine learning solutions supporting defense and national security missions. This role focuses on execution in oversight, ideal for an engineer who thrives in the code, enjoys building end-to-end pipelines, and takes pride in seeing their work directly impact operational systems.

FTI Defense delivers mission-focused solutions to the Department of Defense/Depratment of War (DoD/DoW) and Intelligence Community (IC) through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.

Responsibilities
  • Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
  • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
  • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
  • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
  • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
  • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
  • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
  • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
  • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
  • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.
Education/Qualifications

Minimum Requirements:

  • Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed.
  • 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments.
  • Minimum of 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW) AI assurance, security, and deployment environments.
  • Strong Python development skills with hands-on experience building AI/ML solutions.
  • Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.
  • Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent.
  • Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).
  • Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.
  • Professional experience integrating AI capabilities into production systems or mission applications.

 Preferred Qualifications:

  • Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning.
  • Understanding of prompt engineering, retrieval quality, and grounding methods.
  • Exposure to GPU-based or edge inference environments.
  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
  • Active Secret clearance preferred; ability to obtain one is required.

#LI-KM1

#LI-Remote

Employment Type: FULL_TIME

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