| AI Software Engineer-Princeton NJ NYC***Day 1 Onsite***Immediate Interview-HCL at Princeton, New Jersey, USA |
| Email: [email protected] |
| From: Danish Azeez, Inherent technologies [email protected] Reply to: [email protected] Position: AI Software Engineer Location: Princeton NJ/ NYC, NY ***Day 1 Onsite*** Duration: 1 Years Phone & Skype Client: HCL Immediate Interview *ITAP Reference number BR Number Candidates Details Full Name (First ,Middle, Last) Current Location (City & State) Relocation Authorization (H-1,GC,US Citizen, TN,OPT) Primary Phone Number Email ID SSN (Last 4 digit) DOB (MM/DD) Total IT Experience Total US Experience Vendor Name Salary / Hourly Rate Skill Rating Mandatory Skills Hands on experience in Years Last used -Year Self-Rating (Scale 1-10) 1. 2. 3. 4. 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. Keywords: continuous integration continuous deployment artificial intelligence machine learning materials management information technology green card trade national Idaho New Jersey New York Tennessee |
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| Tue Feb 13 22:42:00 UTC 2024 |