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Automl Jobs (NOW HIRING)

Data Architect

Frisco, TX · On-site

$59.75 - $77/hr

Experience with MLflow, AutoML, or basic ML-enabled analytics use cases. Experience with Power BI/Tableau semantic layer design and governed reporting datasets. Experience with CI/CD pipelines ...

Preferred Qualifications- Experience with cloud-native AI services (e.g., model hosting, autoML, vector search, GPU workloads).- Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker Pipelines ...

Databricks expertise to drive platform adoption and accelerate the development of new use cases, supporting model automation, AutoML, and template-based development. • Hands-on: Advanced data ...

Drive AutoML Innovation: Scale enterprise-grade managed AutoML offerings for tabular and time-series data to radically reduce solution time-to-market from weeks to days. * Engineer AI-Driven Controls:

Drive AutoML Innovation: Scale enterprise-grade managed AutoML offerings for tabular and time-series data to radically reduce solution time-to-market from weeks to days. * Engineer AI-Driven Controls:

Product Marketing Manager

OR · On-site +1

$153K/yr

Position AutoML, experiment tracking, deployment, monitoring, and CI/CD * Build buyer narrative for DS leaders, ML platform teams, and practitioners * Compete against full-lifecycle platforms and ...

Senior Lead AI Engineer (MLXT)

Mclean, VA · On-site

$229.90 - $262.40/hr

Drive AutoML Innovation: Scale enterprise-grade managed AutoML offerings for tabular and time-series data to radically reduce solution time-to-market from weeks to days.* Engineer AI-Driven Controls:

Drive AutoML Innovation: Scale enterprise-grade managed AutoML offerings for tabular and time-series data to radically reduce solution time-to-market from weeks to days. * Engineer AI-Driven Controls:

Senior Data Scientist

Saint Louis, MO · On-site

$90 - $120/hr

You will design and analyze A/B tests and apply advanced techniques such as causal inference, matching models, and AutoML to generate reliable, actionable insights. Partnering closely with business ...

Showing results 41-60

Automl information

What is AutoML?

AutoML, or Automated Machine Learning, refers to the process of automating the end-to-end tasks of applying machine learning to real-world problems. This includes steps like data preprocessing, feature selection, algorithm selection, and hyperparameter tuning. AutoML tools are designed to make machine learning more accessible to non-experts and to improve efficiency for experts by reducing the manual effort and expertise needed to build effective models. Popular AutoML platforms include Google Cloud AutoML, H2O AutoML, and Auto-sklearn.

What are the key skills and qualifications needed to thrive as an AutoML engineer, and why are they important?

To thrive as an AutoML Engineer, you need strong proficiency in machine learning, data science, and programming (often Python), typically supported by a degree in computer science, data science, or a related field. Familiarity with AutoML platforms (such as Google AutoML, H2O.ai, or AutoKeras), cloud services, and experience with ML frameworks like TensorFlow or scikit-learn are essential. Analytical thinking, problem-solving abilities, and effective communication help you translate business needs into automated solutions and collaborate with cross-functional teams. These skills are vital for efficiently developing robust, scalable machine learning pipelines that accelerate model deployment and drive business value.

What are some common challenges faced by professionals working in AutoML roles, and how can they be addressed?

Professionals in AutoML roles often encounter challenges related to automating complex machine learning workflows, ensuring model interpretability, and managing large-scale data pipelines. Balancing automation with customization to meet specific business needs can be tricky, as off-the-shelf solutions may not fit every scenario. Collaborating closely with data scientists, engineers, and domain experts helps in customizing AutoML solutions and overcoming integration issues. Staying updated on the latest tools and frameworks and continuously testing models in production are also essential for success.

What is the difference between Automl vs Data Scientist?

AspectAutomlData Scientist
Required CredentialsTypically certifications in machine learning, data analysis, or related toolsDegree in data science, statistics, computer science, or related fields
Work EnvironmentFocus on developing and deploying automated machine learning models, often in tech or AI companiesAnalyze data, build models, and generate insights across various industries
Employer & Industry UsageUsed by companies seeking scalable ML solutions, including tech, finance, and healthcareEmployed across industries for data analysis, predictive modeling, and decision support

Automl focuses on automating machine learning processes, making it easier to develop models without extensive coding. Data Scientists, however, perform in-depth data analysis, model building, and interpretation. While Automl tools assist Data Scientists, their roles differ in scope and expertise required.

What are the most commonly searched types of Automl jobs?

The most popular types of Automl jobs are:

Infographic showing various Automl job openings in the United States as of August 2026, with employment types broken down into 2% Internship, 95% Full Time, and 3% Contract. Highlights an 88% Physical, 1% Hybrid, and 11% Remote job distribution.

$59.75 - $77/hr

Other

Re-posted 7 days ago


Key responsibilities

  • Design and own the end-to-end revenue data platform on Databricks, implementing the medallion architecture using Delta Lake, Unity Catalog, and Databricks Workflows.

  • Lead the migration of legacy workflows, spreadsheets, and ETL processes into version-controlled, testable dbt models with CI/CD pipeline controls.

  • Architect access controls, change management workflows, and audit trails to ensure SOX compliance, including security and data traceability.


Job description

Hope you are doing good!!!!

My name is Pavan and I work with SPAR Information System., I have a great opportunity for you, please find the job details below, if you are interested in applying please send me your updated resume and best time for you to discuss about this opportunity in details.

Role: Data Architect

Location: Bellevue WA or Frisco, TX

Duration: Long term contract


The Principal Data & Solutions Architect will serve as the primary technical and solution architecture owner of the Revenue Data Management Platform (RDMP), a modernization initiative focused on consolidating revenue data into a governed, centralized Databricks lakehouse. This role partners directly with Revenue, Finance, and D&I COE to architect a medallion data platform, retire legacy workflows (including Alteryx), establish SOX-compliant data infrastructure, and enable advanced analytics - improving reconciliation timelines and enabling trusted, repeatable reporting across the enterprise.

KEY RESPONSIBILITIES

Platform Architecture - Design and own the end-to-end revenue data platform on Databricks, implementing the DR COE medallion architecture (Bronze, Silver, Gold) using Delta Lake, Unity Catalog, and Databricks Workflows.

Alteryx Retirement / Modernization - Lead the migration of Alteryx workflows, manual spreadsheets, and legacy ETL processes into version-controlled, testable dbt models. Translate complex revenue and finance transformation logic - including billing revenue, usage revenue, deferred revenue, and reconciliation logic - into SQL-native models with CI/CD pipeline controls.

SOX & Governance - Architect access controls, change management workflows, and audit trails aligned to ITand SOX requirements. Enforce row-level and column-level security through Unity Catalog, design segregation of duties, and ensure end-to-end traceability of revenue data from source systems to reporting layers.

Data Modeling - Build and certify Silver and Gold layer data products supporting billing revenue, usage-based revenue, revenue adjustments, deferred revenue, revenue reconciliation, and downstream finance reporting. Define data contracts, automated testing frameworks, referential integrity checks, and canonical data definitions. Experience in MDM and driving end-to-end solutions from problem statements is required.

Ingestion & Orchestration - Design ADF-based ingestion pipelines from ERP, billing, CRM, and source systems into the Bronze layer. Orchestrate pipelines using Databricks Workflows with monitoring, alerting, and scalability considerations.

Semantic Layer & Reporting - Architect governed semantic layers powering Power BI and Tableau dashboards for revenue reporting, reconciliation, FP&A insights, and executive reporting. Ensure reporting assets are certified, governed, and audit-ready.

AI & ML Capabilities - Enable AI/ML-driven insights where applicable, including anomaly detection for revenue variances, predictive trends, and assisted analytics, leveraging Databricks capabilities such as MLflow and AutoML.

Stakeholder Partnership - Translate Revenue, Finance, and FP&A requirements into architecture and data product specifications. Lead design reviews, support UAT for certified data products, and communicate effectively with both technical and business stakeholders.

Standards & Mentorship - Establish architecture standards, data modeling practices, and governance frameworks across the RDMP program. Mentor engineering teams on Databricks, dbt, and best practices aligned with DR COE.

REQUIRED QUALIFICATIONS

12+ years of experience in data architecture, solution architecture, or data engineering.

5+ years of hands-on experience with Databricks (Delta Lake, Unity Catalog, Workflows, SQL, Python).

Strong experience with dbt including modeling patterns, testing, and CI/CD integration.

Strong SQL and Python skills for data transformation and pipeline development.

Experience designing SOX-compliant / audit-ready data platforms, including access control and lineage.

Proven experience modernizing legacy tools (Alteryx, SSIS, Informatica, spreadsheets) into modern data platforms.

Experience with Azure ecosystem (ADF, Azure DevOps, Entra ID).

Experience architecting data products for Revenue, Finance, Billing, or FP&A stakeholders.

Strong understanding of revenue data concepts including billing, revenue reconciliation, deferred revenue, and reporting.

Strong communication skills with both technical and business stakeholders.

PREFERRED QUALIFICATIONS

Domain experience in Revenue Data Management, billing systems, revenue reconciliation, deferred revenue, and financial reporting.

Databricks certifications (Data Engineer, Spark Developer, or equivalent).

Experience with MLflow, AutoML, or basic ML-enabled analytics use cases.

Experience with Power BI/Tableau semantic layer design and governed reporting datasets.

Experience with CI/CD pipelines (GitLab/Azure DevOps) for data workloads.

Familiarity with DR COE architecture, data governance, and enterprise security/compliance standards.

Exposure to natural-language query tools (e.g., Databricks Genie) for analytics enablement.

Thanks & Regards,

Pavan Raikhelkar

LEAD TALENT ACQUISITION SPECIALIST

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