Overview
On Site
$40 - $56
Full Time
Skills
ELT
Extract
Transform
Load
Machine Learning Operations (ML Ops)
Machine Learning (ML)
Python
Job Details
Our client is scaling production ML systems and needs a hands-on engineer to help build, maintain, and run essential ML data pipelines. You ll own high-throughput data ingestion and transformation workflows (including image- and array-type modalities), enforce rigorous data quality standards, and partner with research and platform teams to keep models fed with reliable, versioned datasets.
- Design, build, and operate reliable ML data pipelines for batch and/or streaming use cases across cloud environments.
- Develop robust ETL/ELT processes (ingest, validate, cleanse, transform, and publish) with clear SLAs and monitoring.
- Implement data quality gates (schema checks, null/outlier handling, drift and bias signals) and data versioning for reproducibility.
- Optimize pipelines for distributed computing and large modalities (e.g., images, multi-dimensional arrays).
- Automate repetitive workflows with CI/CD and infrastructure-as-code; document, test, and harden for production.
- Collaborate with ML, Data Science, and Platform teams to align datasets, features, and model training needs.
Minimum Qualifications:
5+ years building and operating data pipelines in production.
- Cloud: Hands-on with AWS, Azure, or Google Cloud Platform services for storage, compute, orchestration, and security.
- Programming: Strong proficiency in Python and common data/ML libraries (pandas, NumPy, etc.).
- Distributed compute: Experience with at least one of Spark, Dask, or Ray.
- Modalities: Experience handling image-type and array-type data at scale.
- Automation: Proven ability to automate repetitive tasks (shell/Python scripting, CI/CD).
- Data Quality: Implemented validation, cleansing, and transformation frameworks in production.
- Data Versioning: Familiar with tools/practices such as DVC, LakeFS, or similar.
- Languages: Fluent in English or Farsi.
- Strongly PreferredSQL expertise (writing performant queries; optimizing on large datasets).
- Data warehousing/lakehouse concepts and tools (e.g., Snowflake/BigQuery/Redshift; Delta/Lakehouse patterns).
- Data virtualization/federation exposure (e.g., Presto/Trino) and semantic/metadata layers.
- Orchestration (Airflow, Dagster, Prefect) and observability/monitoring for data pipelines.
- MLOps practices (feature stores, experiment tracking, lineage, artifacts).
- Containers & IaC (Docker; Terraform/CloudFormation) and CI/CD for data/ML workflows.
- Testing for data/ETL (unit/integration tests, great_expectations or similar).
- Soft SkillsExecutes independently and creatively; comfortable owning outcomes in ambiguous environments.
- Proactive communicator who collaborates cross-functionally with DS/ML/Platform stakeholders.
Location: Seattle, WA
Duration: 1+ year
Pay: $56/hr
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