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Mlops Data Engineer Jobs in Arizona (NOW HIRING)

... MLOps platforms (Databricks, MLflow); establish CI/CD pipelines, version control, testing, and monitoring to ensure model quality and reliability * Partner with software engineers, data engineers ...

AIML Engineer Job Location: Scottsdale - Arizona - USA Job Type: Contract to Hire ... Design and implement scalable MLOps supportive data pipelines for data ingestion processing and ...

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Contribute to EP's Enterprise Context Engine - the governed, zero-data-retention AI context layer exposed via MCP to Tabnine Agent and Claude Code. * MLOps & Production Engineering * Build and ...

Contribute to EP's Enterprise Context Engine - the governed, zero-data-retention AI context layer exposed via MCP to Tabnine Agent and Claude Code. * MLOps & Production Engineering * Build and ...

Showing results 21-40

Mlops Data Engineer information

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 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 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 ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

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:

Principal Data Scientist

Insight Enterprises

Phoenix, AZ • On-site

$150 - $210/hr

Other

Posted 7 days ago


Insight Enterprises rating

8.2

Company rating: 8.2 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

57th of 225 rated it services


Job description

Requisition Number: 105936Principal Data ScientistFocusClinical / HLS, Azure ML and DatabricksLocationYou will have the flexibility to work fully remotelyInsight at a Glance14,000+ engaged teammates globally$8.2 billion in revenue in 2025Certified as a Great Place to work in 9 Countries in 2025Fortune 500 Company (No. 447) in 2025Received 25+ industry and partner awards in the past year$1.4M+ total charitable contributions in 2024 by Insight globallyAbout the RoleNow is the time to bring your expertise to Insight. Healthcare and life sciences organizations are investing heavily in analytics, machine learning, and AI, but many still struggle to turn fragmented clinical and operational data into scalable, governed, production-ready solutions.We are seeking a Principal Data Scientist with deep clinical or healthcare and life sciences expertise, strong Azure Machine Learning experience, and hands-on Databricks capability. In this client-facing consulting role, you will help healthcare organizations design, develop, evaluate, and operationalize advanced analytics and AI solutions across modern cloud data platforms.You will work at the intersection of data science, clinical context, cloud architecture, and enterprise delivery. You will help clients move from experimentation to measurable business and clinical impact while ensuring solutions are secure, explainable, reproducible, and aligned to healthcare standards and stakeholder expectations.What You'll DoClinical Data Science Leadership: Lead the design and delivery of data science solutions for healthcare and life sciences use cases, including clinical analytics, predictive modeling, operational intelligence, population health insights, and workflow optimization.Azure ML Solution Development: Design machine learning workflows using Azure Machine Learning, including experimentation, model training, model registry, deployment, monitoring, evaluation, and lifecycle management.Databricks ML and Lakehouse Enablement: Build and guide ML solutions on Databricks, using notebooks, feature engineering, MLflow, Delta Lake, and scalable data pipelines to support healthcare AI and analytics use cases.End-to-End ML Delivery: Translate business and clinical questions into data science problem statements, develop modeling approaches, validate outputs, and partner with engineering teams to productionize solutions.Data Quality and Feature Readiness: Assess clinical and operational data readiness, identify data gaps, define feature strategies, and establish repeatable approaches for lineage, quality checks, and model reproducibility.Responsible AI and Healthcare Governance: Define model evaluation strategies that address performance, bias, explainability, safety, drift, PHI considerations, and stakeholder trust.Client Advisory and Stakeholder Engagement: Serve as a senior advisor to clinical, technical, and executive stakeholders, helping them understand tradeoffs, risks, value drivers, and practical adoption paths for AI and ML solutions.Practice Enablement: Mentor data scientists and engineers while contributing reusable healthcare ML patterns, Databricks accelerators, Azure ML templates, and delivery best practices for Insight.What We’re Looking ForExperience: 10+ years of experience in data science, machine learning, healthcare analytics, AI solution delivery, or enterprise data platforms, ideally in a consulting or client-facing advisory role.Healthcare / HLS Expertise: Strong understanding of healthcare data, clinical workflows, operational healthcare analytics, provider environments, patient data, or life sciences use cases.Azure ML Expertise: Hands-on experience with Azure Machine Learning, including experiment tracking, model management, deployment patterns, monitoring, and integration with broader Azure services.Databricks Expertise: Strong experience with Databricks for data engineering, analytics, feature development, MLflow, Delta Lake, and collaborative data science workflows.Applied ML Depth: Strong foundation in predictive modeling, NLP, classification, regression, clustering, feature engineering, experiment design, and model validation.MLOps and Production Readiness: Experience operationalizing ML models with CI/CD, version control, model registry, reproducibility, monitoring, and governance practices.Responsible AI Mindset: Familiarity with model transparency, AI risk management, PHI-sensitive environments, auditability, and healthcare-specific governance expectations.Consulting Mindset: Strong executive communication skills with the ability to simplify technical concepts and shape practical, business-aligned AI roadmaps.Preferred CertificationsMicrosoft / Azure: Azure AI Engineer Associate, Azure Data Scientist Associate, Azure Solutions Architect Expert, or relevant Microsoft AI and data certifications.Databricks: Databricks Machine Learning Professional, Databricks Data Engineer, or lakehouse-related certifications.Healthcare / Governance: HIPAA, Responsible AI, clinical analytics, or AI governance training is a plus.What You Can ExpectWe’re legendary for taking care of you, your family and to help you engage with your local community.But what really sets us apart are our core values of Hunger, Heart, and Harmony, which guide everything we do, from building relationships with teammates, partners, and clients to making a positive impact in our communities.Join us today, your ambITious journey starts here.Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.At Insight, we celebrate diversity of skills and experience so even if you don’t feel like your skills are a perfect match - we still want to hear from you!Insight does not accept unsolicited resumes from recruiters or employment agencies. Unsolicited resumes will be treated as direct applications from the candidate, and recruiters or agencies who submit candidates for this position without a prior, written vendor agreement will not be eligible for any form of compensation, even if the candidate is hired.The position described above provides a summary of some the job duties required and what it would be like to work at Insight. For a comprehensive list of physical demands and work environment for this position, click here.Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.Posting Notes: AZ-Home || Arizona (US-AZ) || United States (US) || Data & AI || None || Remote || #J-18808-Ljbffr

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