AI Data Engineer

Job summary

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications.

Benefits

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Six or more years of data engineering experience, with significant work supporting ML or AI workloads.
• Strong proficiency in Python and at least one JVM or systems language.
• Deep experience with modern data processing frameworks such as Spark, Ray, or Beam.
• Hands-on experience operating petabyte-scale storage and pipeline systems.
• Strong understanding of distributed systems, data modeling, and storage formats.
• Experience with dataset versioning, lineage, and reproducibility for ML workflows.
• Familiarity with high-throughput data loading for accelerator-based training.
• Strong software engineering practices including testing, CI/CD, and code review.
• Excellent communication and cross-functional collaboration
• Experience with multimodal datasets at large scale.
• Familiarity with data quality tooling and dataset evaluation methodology.

Responsibilities

• Design and operate large-scale data pipelines supporting AI training, evaluation, and continual improvement workflows.
• Build ingestion systems for diverse modalities including text, image, audio, video, and structured signals.
• Implement data cleaning, deduplication, filtering, and quality assurance at petabyte scale.
• Develop dataset versioning, lineage, and provenance tracking systems suitable for reproducible training.
• Build high-throughput data loading systems that maximize GPU utilization during training.
• Implement labeling workflows, active learning pipelines, and human-in-the-loop data improvement systems.
• Design storage architectures balancing cost, throughput, and latency across data tiers.
• Build evaluation dataset construction pipelines with strict integrity and contamination controls.
• Implement data privacy, redaction, and consent enforcement throughout the pipeline.
• Collaborate with ML researchers and engineers to align data systems with model development needs.
• Drive observability of data quality, drift, and pipeline health across the AI data estate.
• Optimize cost and performance through compression, format selection, and caching strategies.

Skills

Degrees

Industry

Company size