Lead AI Data Engineer

Unisoft Technology Inc

United States

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

4 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Amazon Web Services Information Engineering Python (Programming Language) Cloud Services Tensorflow Azure Machine Learning Pytorch Large Language Models Prompt Engineering Containerization Scikit Learn

+5 more

Kubernetes Information Technology HuggingFace Machine Learning Operations Docker

Requirements

Bachelor’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’'’s 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.