| AI Lead/ Cloud Lead Data Engineer for Machine Learning || Princeton NJ/ NYC (Day 1 Onsite ) at Princeton, New Jersey, USA |
| Email: [email protected] |
| Hello, My name is Divya Pandey. and I am a Staffing Specialist at Resource Logistics. I am reaching out to you on an exciting job opportunity with one of our clients. COMPLETE JOB DESCRIPTION IS BELOW FOR YOUR REVIEW: Job Title: Cloud Lead Data Engineer for Machine Learning/ AI Software Engineer/Databricks Feature Store Implementation Location: Princeton NJ/ NYC (Day 1 Onsite ) Hire Type: Contract Your Job As a Data Engineer supporting Machine Learning (ML) initiatives, you will be responsible for using the Databricks Lakehouse Platform to complete advanced data engineering tasks. You will work closely with our data scientists and ML engineers to ensure that data is available, reliable, and optimized for their needs. Key Responsibilities: 1. Cloud Data Architecture: Design and build robust data pipelines using Spark SQL and Python in both batch and incrementally processed paradigms orchestrated via Azure Data Factory. 2. Feature Engineering: Collaborate with data scientists to understand the features needed for ML models. Implement feature extraction and transformation logic in the data pipelines. 3. FeatureOps: Implement FeatureOps to manage the lifecycle of features including their discovery, validation, and serving for training and inference purposes. 4. Training Dataset Support: Work with data scientists to understand their requirements for training datasets. Ensure that these datasets are accurately prepared, cleaned, and made available in a timely manner. 5. Data Pipeline Automation: Automate the data pipelines using CI/CD approaches to ensure seamless deployment and updates. This includes automating tests, deployments, and monitoring of these pipelines. 6. Data Quality: Implement data quality frameworks and monitoring to ensure high data accuracy and reliability. Identify and resolve any data inconsistencies or anomalies. 7. Collaboration: Work closely with data scientists and ML engineers to understand their data needs. Provide them with the necessary data in the right format to facilitate their work. 8. Optimization: Continually optimize pipelines and databases for improved performance and efficiency. This includes implementing real-time processing where necessary. 9. Data Governance: Ensure compliance with data privacy regulations and best practices. Implement appropriate access controls and security measures. 10. Data APIs Qualifications: - Experience supporting machine learning projects. - Familiarity with ML platforms (e.g., TensorFlow, PyTorch). - Experience with cloud platforms (e.g., Azure, AWS). - Bachelor's degree in Computer Science, Engineering, or a related field. - Proven experience as a Data Engineer or in a similar role. - Experience with big data tools (e.g., Hadoop, Spark) and databases (e.g., SQL, NoSQL). - Knowledge of machine learning concepts and workflows. - Strong programming skills (e.g., Python, Java) - Excellent problem-solving abilities and attention to detail. - Strong communication skills to effectively collaborate with other teams. Thanks & Regards, Divya Pandey, Technical Recruiter |Email: [email protected] Resource Logistics, Inc. -- Keywords: continuous integration continuous deployment artificial intelligence machine learning access management information technology New Jersey |
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| Tue Feb 13 21:40:00 UTC 2024 |