Leru Remote
Leru Remote
Leru Remote
About the job
- Location: Remote (Global)
- Employment Type: Full-Time
- Client: Scaling US Technology Product Company
- Department: Engineering / Data Science
- Reports to: Lead AI Architect / Head of Data Engineering
- Job Overview
As an AI Data Engineer, you will design, build, and optimize data pipelines, architectures, and data platforms to support scalable and high-performing AI and ML systems. You’ll work closely with data scientists, ML engineers, and software developers to ensure data is accessible, clean, and ready for modeling, while maintaining compliance, performance, and integrity
Key Responsibilities
- Design, implement, and manage ETL/ELT pipelines to collect, transform, and load structured and unstructured data from multiple sources.
- Develop and maintain data architectures (e.g., databases, large-scale processing systems, data lakes).
- Collaborate with ML engineers and data scientists to prepare data for training and inference.
- Implement data quality checks, monitoring, versioning, and lineage tracking.
- Optimize data storage and retrieval for AI workloads, ensuring scalability and performance.
- Integrate APIs and third-party data systems into our data infrastructure.
- Work with cloud platforms (AWS, GCP, Azure) to deploy scalable AI/ML data pipelines.
- Ensure data security, compliance, and governance best practices.
- Required Skills & Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
- 3+ years of experience in data engineering or backend development.
- Proficiency in Python or Scala, and SQL.
- Experience with data pipeline frameworks (e.g., Apache Airflow, DBT, Luigi).
- Knowledge of big data technologies (e.g., Spark, Kafka, Hadoop).
- Familiarity with ML/AI model pipelines and feature engineering workflows.
- Strong experience with cloud platforms (AWS, GCP, or Azure).
- Experience with containerization tools like Docker and orchestration platforms like Kubernetes is a plus.
- Understanding of MLOps and model deployment best practices is a plus.
Preferred Qualifications
- Experience working in an AI-first or product-focused company.
- Hands-on experience with feature stores (e.g., Feast, Tecton).
- Knowledge of data privacy regulations (e.g., GDPR, HIPAA).
- Exposure to deep learning frameworks (e.g., TensorFlow, PyTorch).
Artificial Intelligence Engineer
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