AI Operations Engineer (Python/Distributed Data Systems): Building and operating large-scale data systems for AI training and evaluation pipelines with an accent on data ingestion, transformation, quality assurance, lineage, and high-throughput delivery. Focus on operating petabyte-scale storage and pipeline systems, enabling reproducible ML workflows, and optimizing data loading for accelerator-based training.
Location: 100% remote within the United States
Salary: $150,000–$165,000 annually
Company
hirify.global is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
What you will do
Build and operate large-scale data systems powering AI training and evaluation pipelines.
Develop ingestion and transformation workflows across diverse data modalities.
Ensure data quality, lineage, versioning, reproducibility, and reliable delivery to training jobs.
Operate petabyte-scale storage and pipeline systems for high-throughput accelerator-based training.
Apply testing, CI/CD, code review, and strong software engineering practices.
Collaborate cross-functionally and communicate technical decisions clearly.
Requirements
6+ years of data engineering experience, including significant work supporting ML or AI workloads.
Strong proficiency in Python and at least one JVM or systems language.
Deep experience with Spark, Ray, or Beam.
Hands-on experience operating petabyte-scale storage and pipeline systems.
Strong understanding of distributed systems, data modeling, and storage formats.
U.S. work authorization is required; new H-1B visa petitions cannot be sponsored.
Nice to have
Experience with large-scale multimodal datasets.
Familiarity with data quality tooling and dataset evaluation methodology.
Exposure to privacy-preserving data systems and regulated data handling.
Open-source contributions to data infrastructure projects.
Experience supporting frontier model training pipelines.
Culture & Benefits
Full-time direct W-2 employment.
Career growth opportunities within an established organization.
Remote work arrangement within the United States.
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