AI Data Engineer

The AI Data Engineer is a critical role that bridges the gap between data science and engineering, enabling the development and deployment of data assets with integrated models. This role is responsible for building and maintaining the data infrastructure and pipelines that power AI and machine learning applications. As an AI Engineer, you will be at the forefront of technological innovation, working with advanced tools and platforms to transform and optimize data processes.

Key Responsibilities:

• Data Ingestion and Processing:
  Design and implement robust data pipelines to collect, clean, transform, and store large volumes of data from diverse sources.
• Data Preparation:
  Preprocess and transform data into formats suitable for machine learning algorithms.
• Feature Engineering:
  Develop and select relevant features that improve the performance of machine learning models.
• Data Support for Models:
  Collaborate with Data Scientists, ML Engineering Professionals, and other cross-functional teams to integrate AI solutions into systems and processes. This includes optimizing AI solutions to handle large volumes of data efficiently.
• Scalability:
  Design and implement scalable data architectures to support the growing demands of AI applications.
• Performance Optimization:
  Continuously optimize data processing and model inference for efficiency and speed.

Required Skills:

• Strong Programming
  
  Skills:
  
  Proficiency in Python, Java, or Scala.
• Data Engineering Tools:
  Experience with Big Data (Big Query, Hive), processing unstructured and structured data, understanding of call center data, media data processing, clickstream, and real-time data processing. Experience with ETL tools like Databricks or equivalent.
• Cloud Platforms:
  Expertise with GCP cloud environment.
• Machine Learning Frameworks:
  Familiarity with Tensor Flow, PyTorch, Vertex AI, or other ML frameworks.
• Data Modeling:
  Understanding of data modeling techniques and database design.
• Data Pipelines:
  Experience with data pipeline orchestration tools like Apache Airflow.
• Problem Solving:
  Ability to analyze and solve complex data engineering challenges.

Qualifications:

• Bachelor's or master’s degree in computer science, Data Science, or a related field.
• Experience with machine learning and AI applications.
• 5+ years in technology and data engineering or a related role.
• 3+ years of experience in cloud.

Desired Skills:

• Experience with MLOps (Machine Learning Operations) practices.
• Knowledge of containerization technologies like Docker and Kubernetes.
• Familiarity with data visualization tools.
• Strong communication and collaboration skills.
• Experience working with Google Cloud Platform.
• Extensive data engineering experience.
• Understanding and familiarity with AI models.

The AI Data Engineer is a key enabler of innovation, powering the development of intelligent applications that transform businesses and industries.