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Day Azure Databricks Jobs in California (NOW HIRING)

Symhas

GEN AI Lead

Danville, CA · On-site

Work Schedule This is a contract role requiring 2 days onsite per week . Candidates must be able to ... AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI ...

Symhas

GEN AI Lead

San Leandro, CA · On-site

Work Schedule This is a contract role requiring 2 days onsite per week . Candidates must be able to ... AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI ...

Lam Research Corporation

Data Engineer 4

Fremont, CA · On-site

$119K - $261K/yr

Experience with Microsoft Fabric, Azure Databricks, Azure Data Factory, Synapse, or related Azure ... 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work ...

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All JobsDay Azure Databricks JobsDay Azure Databricks Jobs in California

Day Azure Databricks information

What is the difference between Day Azure Databricks vs Data Engineer?

AspectDay Azure DatabricksData Engineer
Primary FocusDeveloping and managing data workflows using Azure Databricks platformBuilding, maintaining, and optimizing data pipelines and infrastructure
Skills & CertificationsAzure certifications, Spark, SQL, Python, data analyticsSQL, ETL tools, cloud platforms, programming languages like Python or Java
Work EnvironmentCloud-based, collaborative data platform within Azure ecosystemVaries; cloud, on-premises, or hybrid environments

Day Azure Databricks specialists focus on leveraging the Azure platform for data processing, while Data Engineers build and maintain the data infrastructure across various environments. Both roles require strong technical skills, but their scope and tools differ, with Day Azure Databricks centered on the Databricks platform and Data Engineers on broader data pipeline development.

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Day Azure Databricks jobs near you

GEN AI Lead

Symhas

Danville, CA • On-site

Contractor

Re-posted 4 days ago

Job description

Generative AI Lead | 6–8 Years Experience

We're looking for a seasoned Machine Learning Engineer who thrives at the intersection of data, engineering, and business impact. If you love turning messy real-world problems into production-grade AI solutions — this one's for you.


Work Schedule

This is a contract role requiring 2 days onsite per week. Candidates must be able to commute to the office location.


What You'll Do

  • Partner directly with business stakeholders to define ML use cases, success metrics, and evaluation frameworks — translating strategy into working models
  • Lead end-to-end data workflows: exploration, quality checks, feature engineering, and dataset preparation
  • Build, train, and iterate on ML models; run experiments, compare candidates, and champion the best solution
  • Package and deploy models into production-ready services using containerization and MLOps best practices
  • Own post-deployment health — set up monitoring, track model performance, and drive continuous improvement


Your Technical Toolkit

Languages & Querying Python (hands-on, non-negotiable) · SQL (joins, window functions, CTEs, query optimization)

Machine Learning Regression · Decision Trees · Random Forest · XGBoost · LightGBM · SVM · KNN Model evaluation (Precision/Recall, F1, ROC-AUC, MSE/RMSE) · Hyperparameter tuning · Cross-validation

Deep Learning TensorFlow · Keras · PyTorch · CNNs · RNNs · LSTMs · Transformers Applied to NLP, Computer Vision, and Time-Series Forecasting

Data Engineering Feature engineering · Missing data handling · Outlier detection · Normalization · Data cleaning pipelines

Visualization & BI Matplotlib · Seaborn · Plotly · Tableau · Power BI · Storytelling with data

Cloud & Big Data Spark · Hadoop · AWS (S3, SageMaker, EC2) or Azure (Databricks, Data Factory) or GCP (BigQuery, Vertex AI)

Deployment & MLOps Flask / FastAPI · Docker · Kubernetes (a plus) · CI/CD basics · Airflow / Prefect

Databases MySQL · PostgreSQL · SQL Server · MongoDB · Cassandra


✅ What Sets You Apart

  • A solid conceptual grip on supervised and unsupervised learning, with real experimental work to back it up
  • Proven experience shipping models to production in cloud-agnostic, API-first architectures
  • Comfortable collaborating with engineering teams via version control and CI/CD workflows
  • Generative AI exposure is a strong plus — and increasingly central to this role
Industry
  • Technology, Information and Internet
Employment Type

Contract