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Assistant Mlops Jobs (NOW HIRING)

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 ...

Novelis Corporate HQ

Head of AI Automation

Atlanta, GA · On-site

$173K - $223K/yr

MLOps & AI Production Engineering * Knowledge Management Solutions (RAG Solutions) * Advanced ... assistants for operations, engineering, and corporate functions. * Build and operate advanced ...

CreditOne

AVP, Artificial Intelligence

Las Vegas, NV · On-site

Description Position Summary The Assistant Vice President of Artificial Intelligence (AVP of AI) is ... Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal ...

MSR Cosmos

Google Cloud ML Engineer

Dallas, TX · On-site

$55.25 - $73.75/hr

Solid MLOps understanding (Docker, Kubernetes, CI/CD, Git). Experience with Agent Assist functionality is a plus. Preferred Qualifications: * Master's/PhD in Computer Science, AI/ML, or related field.

CNA

Senior Data AI Engineer

Chicago, IL · On-site

$118K - $141K/yr

At CNA, we strive to create a culture in which people know they matter and are part of something ... Productionize and operationalize AI solutions and advanced analytics in a DevOps/MLOps environment ...

TMS

AI Architect- Insurance domain

Edison, NJ · Remote

$75/hr

You will define end-to-end AI architecture (data → model → MLOps → serving), ensure secure ... assist, and personalization. * Select and guide model approaches: predictive ML , LLMs/GenAI , NLP ...

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Assistant Mlops information

What are 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 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.

What are some typical daily responsibilities for an Assistant MLOps professional?

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, and why are they important?

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.
More about Assistant Mlops jobs
What cities are hiring for Assistant Mlops jobs? Cities with the most Assistant Mlops job openings:
What are the most commonly searched types of Mlops jobs? The most popular types of Mlops jobs are:
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What job categories do people searching Assistant Mlops jobs look for? The top searched job categories for Assistant Mlops jobs are:
Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 99% Physical, and 1% Remote job distribution.

Databricks AI / ML Engineer (m/w/d)

Qualysoft

On-site, Remote

Contractor

Re-posted 15 days ago

Job description

Fur ein langfristig angelegtes Daten- und KI-Programm wird ein Databricks AI / ML Engineer (m/w/d) gesucht.
Ziel ist die Entwicklung, Implementierung und der Betrieb skalierbarer Machine-Learning- und LLM-Losungen auf Azure Databricks von der Datenaufbereitung uber Feature Engineering bis hin zu MLOps, Deployment und Monitoring.
Der Fokus liegt auf Big-Data-Engineering, ML/LLM-Workloads, MLOps-Automatisierung sowie der nahtlosen Integration in das Microsoft-Okosystem.
520 - 560 a day
Rahmenbedingungen
Start: Marz/April 2026
Laufzeit: 3 Jahre (optional verlangerbar bis max. 5 Jahre)
Auslastung: 100 %
Arbeitsmodell: Hybrid - ca. 50 % vor Ort in Wien, ca. 50 % remote
Projektsprache: Deutsch und Englisch

Aufgaben:
Datenanalyse und Prototyping mit Python in Azure Databricks unter Einsatz gangiger ML-Frameworks
Entwicklung und Betrieb von Big-Data-Pipelines mit Apache Spark, Delta Lake und Databricks SQL
Durchfuhrung von Feature Engineering sowie Training, Versionierung und Deployment von Modellen mit Databricks MLflow
Entwicklung und Betrieb von ML- und LLM-Workloads auf Azure Databricks (inkl. Unity Catalog, Performance- und Kostenoptimierung)
End-to-End-Integration der Losungen in das Microsoft-Okosystem (z. B. API- und Schnittstellendesign, Orchestrierung mit Azure Functions und Logic Apps)
Aufbau und Weiterentwicklung von MLOps- und CI/CD-Pipelines fur automatisiertes Training, Testing, Deployment und Monitoring von ML- und LLM-Modellen sowie Agents
Durchfuhrung von Modell- und Datenmonitoring (Modellleistung, Daten-Drift, Bias) inklusive Wartungs- und Updateprozessen
Einsatz von AutoML-Tools zur Beschleunigung von Prototypen und Experimenten
Sicherstellung von Skalierbarkeit, Sicherheit und stabilen Betriebsprozessen der entwickelten Losungen

Fachliche Anforderungen:
Fundierte Kenntnisse in Datenanalyse und Prototyping mit Python in Azure Databricks
Erfahrung mit Machine-Learning-Frameworks wie TensorFlow, PyTorch und scikit-learn
Praktische Erfahrung im Big Data Engineering mit Apache Spark, Delta Lake und Databricks SQL
Kompetenz in Feature Engineering sowie Modell-Deployment mit managed Databricks MLflow
Erfahrung in der Entwicklung und dem Betrieb von ML- und LLM-Workloads auf Azure Databricks
Erfahrung mit End-to-End-Integrationen im Microsoft-Okosystem
Erfahrung im Aufbau von MLOps- und CI/CD-Pipelines fur ML- und LLM-Modelle sowie agentische Workflows
Erfahrung im Modell- und Datenmonitoring (Leistung, Drift, Bias) inklusive passender Wartungsstrategien
Praktische Erfahrung im Einsatz von AutoML-Tools
Vorhandensein einer eigenen, vom Produktivsystem des Auftraggebers getrennten Entwicklungsumgebung, die den aktuellen Standards fur Datensicherheit und Zugriffsschutz entspricht (inkl. Nachweis der Infrastruktur)

PLUS:
Erfahrung mit Data- und KI-Governance
Erfahrung in der Konzeption und Umsetzung agentischer Ansatze (Agenten, Multi-Agent-Systeme, agentische Workflows) mit Azure-Ressourcen
Erfahrung in der Umsetzung von End-to-End-Databricks-Projekten (von Datenaufbereitung und Feature Engineering uber Modelltraining und Deployment bis zu MLOps und Monitoring)
Branchenkenntnisse in der Energieindustrie
Strukturierte und analytische Arbeitsweise
Hohes Qualitatsbewusstsein und Verantwortungsbereitschaft
Sehr gute Kommunikationsfahigkeit gegenuber technischen und fachlichen Stakeholdern
Teamfahigkeit und Bereitschaft zur Wissensweitergabe

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Vielen Dank fur Ihr Interesse!

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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