| AI Software Engineer/Databricks Feature Store Implementation || Cloud Lead Data Engineer for Machine Learning|| Onsite NJ/ NYC || Any VISA || at Princeton, New Jersey, USA |
| Email: [email protected] |
| Hi, I hope you are doing well Please find the Jd mentioned below. AI Software Engineer/Databricks Feature Store Implementation JD attached. Location preferably Princeton NJ/ NYC. If not, then any other location in US will also work. Working hours EST Candidate should have experience on Databricks Feature Store Implementation Short Description - need for Data Engineer for Machine Learning which can also translate to ML Engineer focused on data. Need a lead here who can also guide few other team members. Very strong with core foundational devops skills as it is about dataops (operationalizing) in Azure Cloud Cloud Lead Data Engineer for Machine Learning for the GSI Division Information Technology; 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. Sugam Saurav Email: [email protected] Intellicept Inc - A Division of McKinsol Consulting Keywords: continuous integration continuous deployment artificial intelligence machine learning information technology golang New Jersey |
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| Tue Feb 13 15:17:00 UTC 2024 |