Lead Data Engineer

Openkyber, LLC; Atlanta (GA); On-site; USD 180,000 - 240,000; Full time

Job summary

Openkyber, LLC is seeking a senior ML platform engineer to design and operate enterprise ML systems, serving multiple teams in production. The role emphasizes Python proficiency, ML frameworks, and strong MLOps tooling experience.

The candidate should have 15+ years in software/data engineering, with 4+ years focused on ML pipelines, LLMs, and cloud-native services on AWS. Onsite interview in MD is noted in the posting.

Qualifications

• 15+ years of experience in software or data engineering, with at least 4 years focused on ML systems or MLOps in production.
• Experience building or operating a shared/enterprise ML platform serving multiple teams or business units.
• Bachelor degree in Computer Science, Data Science, Engineering, or a related field.
• Strong proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face).
• Hands-on experience with MLOps tooling: Kubeflow, MLflow, Airflow, or equivalent.
• Experience with cloud-native data and ML services on AWS.
• Working knowledge of LLMs, prompt engineering, and RAG architecture patterns.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Strong understanding of data engineering concepts: pipelines, feature stores, data quality, and lineage.

Skills

Python

ML frameworks

MLOps tooling

AWS

LLMs / prompt eng

Docker / Kubernetes

Data pipelines

Feature stores

Data quality

Data lineage

Education

Bachelor's degree in CS/DS/Engineering

Tools

Kubeflow

MLflow

Airflow

Docker

Kubernetes

Job description

100% Onsite Role - Onsite interview in MD

Bachelor degree in Computer Science, Data Science, Engineering, or a related field

15+ years of experience in software or data engineering, with at least 4 years focused on ML systems or MLOps in a production environment.

Demonstrated experience building or operating a shared/enterprise ML platform serving multiple teams or business units.

Strong proficiency in Python and familiarity with ML frameworks (e.g., "PyTorch, TensorFlow, scikit-learn, Hugging Face").

Hands-on experience with MLOps tooling: Kubeflow, MLflow, Airflow, or equivalent.

Experience with cloud-native data and ML services on AWS.

Working knowledge of LLMs, prompt engineering, and RAG architecture patterns.

Experience with containerization and orchestration (Docker, Kubernetes).

Strong understanding of data engineering concepts: pipelines, feature stores, data quality, and lineage.