What are the responsibilities and job description for the AI/ML Data Engineer : position at HPTech Inc.?
Job Details
Title : Data Engineer AI/ML
Mountain View, CA (Hybrid)
About the Role:
We are seeking a Data Engineer with strong AI/ML expertise to join our data-driven team. In this role, you will design, build, and optimize data pipelines that support advanced machine learning models and AI-driven applications. You ll collaborate with data scientists, software engineers, and business stakeholders to drive data-centric solutions that power high-impact products and services.
Key Responsibilities:
- Design, develop, and maintain scalable data pipelines and ETL processes to support AI/ML models.
- Integrate and optimize large datasets from multiple sources for analytics, model training, and real-time decision-making.
- Collaborate with data scientists to prepare and transform data for machine learning applications.
- Implement best practices for data quality, governance, and security.
- Work with cloud-based platforms (AWS, Google Cloud Platform, or Azure) and big data technologies like Spark, Kafka, and Hadoop.
- Utilize programming languages such as Python, Scala, or Java for data engineering and AI/ML tasks.
- Apply ML algorithms and frameworks (TensorFlow, PyTorch, Scikit-learn) to solve complex business problems.
- Build and maintain data lakes, data warehouses (Snowflake, Redshift, BigQuery), and real-time data streaming solutions.
- Collaborate with cross-functional teams to translate business requirements into data solutions.
Must-Have Skills:
6 years of experience in Data Engineering.
Strong programming skills in Python (including AI/ML libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
Hands-on experience with AI/ML model deployment and pipeline integration.
Proficiency in SQL, ETL development, and data modeling.
Experience with Big Data technologies (e.g., Spark, Kafka, Hadoop).
Expertise in cloud platforms (AWS, Google Cloud Platform, or Azure) for data engineering and ML workflows.
Strong understanding of data governance, privacy, and security standards.
Knowledge of MLOps practices and tools (Kubeflow, Airflow, MLflow) for model deployment and monitoring.
Nice-to-Have Skills:
- Experience with real-time data processing (Kafka, Kinesis).
- Familiarity with containerization (Docker, Kubernetes) for deploying ML models.
- Experience with data visualization tools (Tableau, Power BI, Looker).
- Knowledge of CI/CD pipelines for ML models.
- Exposure to finance or fintech industry data processes.
Soft Skills:
- Strong problem-solving and analytical thinking.
- Excellent communication skills to collaborate with technical and non-technical teams.
- Ability to work in a fast-paced, agile environment.
- Self-starter with a passion for data and continuous learning.
