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Assistant Mlops Jobs (NOW HIRING)

Qualysoft

Fur ein Enterprise-KI-Projekt wird ein erfahrener MLOps Engineer gesucht. Ziel ist der Aufbau und ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Eliassen Group

MLOps / DevOps Engineer

Charlotte, NC · On-site

$70 - $81/hr

Support and improve MLOps platforms with a focus on reliability, scalability, and automation ... These tools assist our hiring teams in different ways, including but not limited to, assistance in ...

TetraScience

Lead Software Platform Engineer, MLOps

... to assist you in better understanding whether TetraScience is the right fit for you from a values ... You will architect the cloud-based services and MLOps infrastructure that enable production-grade ...

Copart, Inc.

DevOps Engineering Intern

Dallas, TX · On-site

AI / MLOps Support * Assist with deployment and monitoring of machine learning and AI applications. * Support AI workflows including model deployment and inference services. * Collaborate with Data ...

Qualysoft

Der Fokus liegt auf Big-Data-Engineering , ML/LLM-Workloads , MLOps-Automatisierung sowie der ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

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Assistant Mlops information

What is an Assistant MLOps?

Assistant MLOps are professionals who support the deployment, monitoring, and management of machine learning models in production environments. They assist senior MLOps engineers with tasks like automating workflows, managing data pipelines, maintaining infrastructure, and ensuring model performance. Their role bridges the gap between data science and IT operations, helping organizations scale and maintain their AI solutions efficiently. Assistant MLOps often have knowledge of cloud services, CI/CD tools, and basic programming, and they work closely with data scientists and engineers.

What are the typical daily responsibilities of an Assistant MLOps?

As an Assistant MLOps professional, you can expect your daily tasks to involve supporting the deployment, monitoring, and maintenance of machine learning models in production environments. This often includes collaborating with data scientists to automate model training and testing workflows, managing cloud-based resources, and ensuring that data pipelines are running smoothly. You'll also help troubleshoot issues related to model performance or infrastructure and assist in implementing best practices for version control and continuous integration. Working closely with both engineering and data teams, you'll play a key role in ensuring that ML models remain reliable and scalable in real-world applications.

What are the key skills and qualifications needed to thrive as an Assistant MLOps?

To thrive as an Assistant MLOps, you need a solid understanding of machine learning fundamentals, programming (especially Python), and experience with cloud platforms; a degree in computer science or a related field is typically preferred. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and version control systems (e.g., Git) is important, and certifications in cloud services (AWS, Azure, GCP) can be advantageous. Strong problem-solving, communication, and collaboration skills help you bridge the gap between data science and operations teams. These combined skills ensure efficient deployment, monitoring, and maintenance of machine learning models in production environments.

What is the difference between Assistant Mlops vs Data Engineer?

AspectAssistant MlopsData Engineer
Required CredentialsCertifications in cloud platforms, basic scripting, ML toolsComputer science degree, SQL, Python, data architecture
Work EnvironmentCollaborates with ML teams, supports deployment pipelinesBuilds data pipelines, manages databases, processes large datasets
Industry UsageAI/ML projects, cloud-based environmentsData infrastructure, analytics, big data solutions

Assistant Mlops and Data Engineer roles share overlapping skills in cloud platforms and scripting. However, Assistant Mlops focuses on supporting ML deployment and operations, while Data Engineers primarily build and maintain data infrastructure. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

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Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 3% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.

MLOPs Engineer

Compusoft Integrated Solutions, Inc.

Miami, FL • On-site

Other

Posted 9 days ago

Job description

Title: MLOPs Engineer

Location: Hybrid Role (South Florida Preferred)

Duration: 6+ Months (Must be able to convert FTE WITHOUT SPONSORSHIP)

Required Skills:

  • 8+ years of Machine Learning Engineering or applied AI experience.
  • 3+ years in Lead, Principal, or senior technical leadership roles.
  • Strong hands-on Python development for production-grade machine learning solutions.
  • Advanced experience with Databricks, MLflow, and distributed machine learning workloads.
  • Expertise with TensorFlow, PyTorch, Scikit-learn, or similar ML frameworks.
  • Proven experience building and deploying large-scale recommendation engines.
  • Strong experience developing customer personalization and customer intelligence solutions.
  • Experience with customer segmentation, churn prediction, and customer value models.
  • Strong understanding of Customer 360 platforms and unified customer data.
  • Experience using identity graphs to improve customer matching and prediction accuracy.
  • Strong feature engineering, model evaluation, validation, and lifecycle management experience.
  • Experience designing scalable batch and real-time inference architectures.
  • Proven experience deploying, monitoring, and retraining machine learning models in production.
  • Experience partnering with Data Engineering teams to create ML-ready datasets.
  • Strong architecture experience across Data Science, Engineering, and MLOps platforms.
  • Experience leading technical design reviews and establishing enterprise ML standards.
  • Strong mentoring, stakeholder communication, and cross-functional technical leadership skills.

Preferred Skills:

  • Experience with Snowflake and integrated Databricks data environments.
  • Experience building GenAI, LLM-powered, or agentic AI applications.
  • Experience developing domain-specific AI agents and intelligent assistants.
  • Knowledge of MLOps, feature stores, model serving, and automated retraining.

Experience with real-time recommendation and streaming personalization platforms

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