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Mlops Data Engineer Jobs in Arizona (NOW HIRING)

Rivago infotech inc

MLOPS Engineer

Scottsdale, AZ · On-site

$60/hr

MLOPS Engineer Location : Scottsdale AZ (Onsite) Rate : $60 No of roles : 3 Indents : We are ... Collaborate with Data Scientists and Data Engineers for productionizing ML models * Ensure ...

Inficare Technologies

AI Python Engineer

Phoenix, AZ · On-site

Senior AI Python Engineer Location: Phoenix, AZ (Hybrid - 3 days onsite) Job Type: Long-Term ... MLOps/data tools: MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or NiFi Preferred Skills * Flask ...

IntraEdge

Senior AI/ML & IVR Engineer GCP

Scottsdale, AZ · On-site

$105K - $145K/yr

Advanced MLOps: CI/CD, containerization (Docker), orchestration (Kubernetes), monitoring, and automation * Data engineering skills: ETL/ELT, real-time and batch pipelines * Excellent communication ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Automate workflows for data ingestion, model training, deployment, and monitoring. Collaborate with ... Apply MLOps best practices for reproducibility, versioning, and governance of ML models. Required ...

Phoenix Staff

Lead Data & AI Engineer

Phoenix, AZ · On-site +1

$50 - $60/hr

Lead Data & AI Engineer Location: Phoenix, AZ (hybrid remote) Type: 6-month contract to hire Pay ... MLOps practices including versioning, CI/CD, monitoring, and drift detection. · Implement data ...

Deloitte

AI Engineer Consultant

Tempe, AZ · Hybrid

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Accenture

Google Data Specialist

Scottsdale, AZ · On-site

$70K - $196K/yr

Build data pipelines, ETL/ELT processes, and integrations using GCP services such as: BigQuery ... establish MLOps processes (monitoring, retraining, deployment). * Support prompt engineering ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

... engineering, model training, hyperparameter tuning, evaluation, and deployment * Develop scalable data pipelines on Databricks; integrate experimentation and ML systems with modern data and MLOps ...

Champions Funding LLC

Data Scientist

Gilbert, AZ · On-site

Partner with data engineering, IT, and business teams to develop scalable data pipelines and deploy ... AI, MLOps, or related machine learning frameworks. * Experience developing, deploying, and ...

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All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Arizona

Mlops Data Engineer information

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining machine learning models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

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

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What is the salary of MLOps Data Engineer?

The salary of an MLOps Data Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Professionals with advanced skills in cloud platforms, automation, and machine learning tools may earn higher compensation.
What are popular job titles related to Mlops Data Engineer jobs in Arizona? For Mlops Data Engineer jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Mlops Data Engineer jobs in Arizona look for? The top searched job categories for Mlops Data Engineer jobs in Arizona are:
What cities in Arizona are hiring for Mlops Data Engineer jobs? Cities in Arizona with the most Mlops Data Engineer job openings:
Mlops Data Engineer jobs near you

MLOPS Engineer

Rivago infotech inc

Scottsdale, AZ • On-site

$60/hr

Other

Posted 12 days ago

Job description

Role : MLOPS Engineer

Location : Scottsdale AZ (Onsite)

Rate : $60

No of roles : 3

Indents :

We are looking for a skilled MLOps Engineer to design, deploy, and manage scalable machine learning pipelines in production. The role focuses on enabling seamless integration of ML models into enterprise systems with reliability, automation, and governance.

Key Responsibilities

  • Design and implement end-to-end ML pipelines from data ingestion to model deployment
  • Build and manage CI/CD pipelines for ML models (training, testing, deployment)
  • Automate model monitoring, retraining, and performance optimization
  • Collaborate with Data Scientists and Data Engineers for productionizing ML models
  • Ensure scalability, reliability, and security of ML systems
  • Manage model versioning, experiment tracking, and lifecycle management
  • Implement best practices for governance, compliance, and reproducibility

Key Skills & Expertise

  • Strong programming skills in Python
  • Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
  • Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Azure ML
  • Knowledge of CI/CD tools: Jenkins, GitHub Actions, GitLab CI
  • Experience with cloud platforms: AWS
  • Strong understanding of data pipelines, ETL processes, and distributed systems

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