Data Engineer (AI)

Location: Herndon, VA; hybrid workplace

Company

hirify.global develops vision AI solutions for leading retailers, including platforms that process large-scale video and event data.

What you will do

• Design and build data pipelines and platform components for model training, evaluation, and real-time inference.
• Own conceptual, logical, and physical data modeling for analytical and operational use cases.
• Implement cloud-based data lakehouse solutions with product and engineering teams.
• Build low-latency streaming applications that move event and video-derived data from stores to the platform.
• Model, store, and serve large data volumes with Snowflake and Databricks while optimizing reliability, cost, and query performance.
• Establish data quality, lineage, observability, shared tooling, and engineering standards in collaboration with platform, MLOps, analytics, and operations teams.

Requirements

• 3–5 years of experience building production data platforms or pipelines, ideally at scale.
• Strong experience with Snowflake, Databricks, data modeling, performance tuning, and cost management.
• Experience building streaming and real-time data applications with Kafka, Spark Structured Streaming, or Flink.
• Strong Python and SQL programming skills, including data transformations and automation.
• Understanding of distributed systems, data warehousing, lakehouse patterns, and maintainable software engineering practices.
• Experience with cloud platforms, Docker, Kubernetes, CI/CD, data quality, reliability, and observability; a degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice to have

• Experience with large-scale video, event, or other high-volume data sources.
• Familiarity with the broader stack, including C/C++, CUDA, PyTorch, OpenCV, TensorRT, ONNX, Linux, and real-time video processing.

Culture & Benefits

• Full-time permanent employment in a hybrid work environment.
• Opportunity to work with industry experts while scaling a global organization.
• Fast-paced, iterative, delivery-focused environment with independent problem-solving.
• Cross-functional collaboration with applied science, platform, infrastructure, MLOps, product, analytics, and operations teams.