{"schemaVersion":"jobsearcher.job.v1","id":"cb537295ed2e29abb3ec41b2","url":"https://jobsearcher.com/jobs/cb537295ed2e29abb3ec41b2","canonicalUrl":"https://jobsearcher.com/jobs/cb537295ed2e29abb3ec41b2","title":"AIML Architect with Databricks AWS","description":"Job Title: AI/ML Architect with Databricks , AWS\n\nLocation : Los Angeles CA (Hybrid)\n\nHire type : FTE / CTH\n\nRole Overview\nWe are seeking an experienced AI/ML Architect with deep hands‑on expertise in Databricks on AWS to lead the design and implementation of scalable, high-performance data and machine learning platforms. The ideal candidate combines architectural thinking with strong engineering execution, demonstrating the ability to build modern lakehouse systems, optimize large-scale pipelines, and drive analytical and ML capabilities across the organization.\n\nThis role requires working with large, multi-terabyte datasets, advanced analytics, and end-to-end ML lifecycle management using Databricks, Python, PySpark, and AWS-native services.\n\nMust Demonstrate (Critical Competencies)\n\nDesigning Databricks‑based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).\n\nClear separation of compute vs. serving layers in distributed architectures.\n\nLow‑latency API strategy where Spark is insufficient (e.g., leveraging optimized services or caching).\n\nCaching strategies to accelerate reads and reduce compute cost.\n\nData partitioning, file size tuning, and optimization strategies for large-scale pipelines.\n\nExperience handling multi‑terabyte structured time‑series workloads.\n\nAbility to distill architectural significance from ambiguous business requirements.\n\nStrong curiosity, questioning, and requirement‑probing mindset.\n\nPlayer‑coach approach: hands‑on technical depth + ability to guide design.\n\nKey Responsibilities\nAI/ML & Advanced Analytics\n\nDevelop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.\n\nConduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.\n\nDeploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.\n\nBuild analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.\n\nDesign ML architectures aligned with Databricks Lakehouse on AWS.\n\nData Engineering & Lakehouse Architecture\n\nArchitect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.\n\nImplement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.\n\nDesign lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.\n\nOptimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.\n\nWork with multi‑terabyte, time‑series, high‑velocity data in a distributed environment.\n\nEnsure robust data availability for downstream ML and analytics workloads.\n\nAWS Cloud Integration\nArchitect end-to-end data and ML solutions using AWS services, including:\n\nS3 for storage\n\nIAM for identity & access\n\nGlue Catalog for metadata management\n\nNetworking for secure, high‑throughput data movement\n\nIntegrate Databricks with AWS-native compute, API layers, and low‑latency endpoints.\n\nBusiness Collaboration & Leadership\n\nTranslate business problems into scalable analytical or ML architectures.\n\nCommunicate complex statistical and architectural concepts to non‑technical stakeholders.\n\nCollaborate with product, engineering, and business leaders to drive data‑informed initiatives.\n\nProvide design leadership while remaining hands‑on in execution.\n\nSkills & Qualifications\nRequired\n\nBachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.\n\n10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.\n\nDeep expertise in Databricks on AWS, including PySpark / Spark SQL, Databricks Notebooks, Delta Lake, Unity Catalog, MLflow, Databricks Jobs & Workflows.\n\nStrong programming ability in Python (pandas, numpy, scikit‑learn).\n\nDemonstrated experience with large‑scale, multi‑terabyte data processing.\n\nStrong understanding of ML algorithms, distributed systems, and data optimization.\n\nPreferred\n\nExperience with MLOps and production deployment pipelines.\n\nStrong grasp of AWS‑native data and compute services.\n\nUnderstanding of CI/CD using GitHub Actions, GitLab CI, or similar.\n\nFamiliarity with deep learning frameworks (TensorFlow, PyTorch).\n\nKey Competencies\n\nStrong analytical and problem‑solving skills.\n\nAbility to work in fast‑paced, highly collaborative environments.\n\nExcellent communication and presentation abilities.\n\nSelf‑driven with exceptional attention to architectural detail.\n\nFlexible work from home options available.\n\n#J-18808-Ljbffr","company":"Vytwo","rawCompany":"vytwo","city":"Prosper","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-04-09T09:32:37.299Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AIML Architect with Databricks AWS","description":"Job Title: AI/ML Architect with Databricks , AWS\n\nLocation : Los Angeles CA (Hybrid)\n\nHire type : FTE / CTH\n\nRole Overview\nWe are seeking an experienced AI/ML Architect with deep hands‑on expertise in Databricks on AWS to lead the design and implementation of scalable, high-performance data and machine learning platforms. The ideal candidate combines architectural thinking with strong engineering execution, demonstrating the ability to build modern lakehouse systems, optimize large-scale pipelines, and drive analytical and ML capabilities across the organization.\n\nThis role requires working with large, multi-terabyte datasets, advanced analytics, and end-to-end ML lifecycle management using Databricks, Python, PySpark, and AWS-native services.\n\nMust Demonstrate (Critical Competencies)\n\nDesigning Databricks‑based lakehouse architectures on AWS (Delta Lake + S3 + Unity Catalog).\n\nClear separation of compute vs. serving layers in distributed architectures.\n\nLow‑latency API strategy where Spark is insufficient (e.g., leveraging optimized services or caching).\n\nCaching strategies to accelerate reads and reduce compute cost.\n\nData partitioning, file size tuning, and optimization strategies for large-scale pipelines.\n\nExperience handling multi‑terabyte structured time‑series workloads.\n\nAbility to distill architectural significance from ambiguous business requirements.\n\nStrong curiosity, questioning, and requirement‑probing mindset.\n\nPlayer‑coach approach: hands‑on technical depth + ability to guide design.\n\nKey Responsibilities\nAI/ML & Advanced Analytics\n\nDevelop, train, and optimize ML models using Python, PySpark, MLflow, and Databricks Machine Learning.\n\nConduct exploratory data analysis (EDA) to identify patterns, trends, and insights in large datasets.\n\nDeploy ML models into production using MLflow, Databricks Workflows, or other MLOps pipelines.\n\nBuild analytics solutions such as forecasting, anomaly detection, segmentation, or recommendation systems.\n\nDesign ML architectures aligned with Databricks Lakehouse on AWS.\n\nData Engineering & Lakehouse Architecture\n\nArchitect and build scalable ETL/ELT pipelines using PySpark, SQL, and Databricks Workflows.\n\nImplement Delta Lake best practices, including OPTIMIZE, ZORDER, partitioning, and schema evolution.\n\nDesign lakehouse layers (Bronze/Silver/Gold) with strong separation of compute and serving layers.\n\nOptimize cluster performance and jobs using Spark tuning, caching, and shuffle minimization.\n\nWork with multi‑terabyte, time‑series, high‑velocity data in a distributed environment.\n\nEnsure robust data availability for downstream ML and analytics workloads.\n\nAWS Cloud Integration\nArchitect end-to-end data and ML solutions using AWS services, including:\n\nS3 for storage\n\nIAM for identity & access\n\nGlue Catalog for metadata management\n\nNetworking for secure, high‑throughput data movement\n\nIntegrate Databricks with AWS-native compute, API layers, and low‑latency endpoints.\n\nBusiness Collaboration & Leadership\n\nTranslate business problems into scalable analytical or ML architectures.\n\nCommunicate complex statistical and architectural concepts to non‑technical stakeholders.\n\nCollaborate with product, engineering, and business leaders to drive data‑informed initiatives.\n\nProvide design leadership while remaining hands‑on in execution.\n\nSkills & Qualifications\nRequired\n\nBachelor’s or Master’s in Computer Science, Data Science, Engineering, Statistics, or related field.\n\n10+ years of experience in data engineering, ML engineering, or AI/ML architecture roles.\n\nDeep expertise in Databricks on AWS, including PySpark / Spark SQL, Databricks Notebooks, Delta Lake, Unity Catalog, MLflow, Databricks Jobs & Workflows.\n\nStrong programming ability in Python (pandas, numpy, scikit‑learn).\n\nDemonstrated experience with large‑scale, multi‑terabyte data processing.\n\nStrong understanding of ML algorithms, distributed systems, and data optimization.\n\nPreferred\n\nExperience with MLOps and production deployment pipelines.\n\nStrong grasp of AWS‑native data and compute services.\n\nUnderstanding of CI/CD using GitHub Actions, GitLab CI, or similar.\n\nFamiliarity with deep learning frameworks (TensorFlow, PyTorch).\n\nKey Competencies\n\nStrong analytical and problem‑solving skills.\n\nAbility to work in fast‑paced, highly collaborative environments.\n\nExcellent communication and presentation abilities.\n\nSelf‑driven with exceptional attention to architectural detail.\n\nFlexible work from home options available.\n\n#J-18808-Ljbffr","datePosted":"2026-04-09T09:32:37.299Z","dateModified":"2026-04-09T09:32:37.299Z","hiringOrganization":{"@type":"Organization","name":"Vytwo","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Prosper","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"cb537295ed2e29abb3ec41b2"},"url":"https://jobsearcher.com/jobs/cb537295ed2e29abb3ec41b2"}}