Principal Data & ML Ops Architect
New
I
ICA, Inc.Government Health Analytics
Remote work from anywhere within the continental United States, Eastern Standard Time (EST)Full-TimePrincipal
Salary170,000 - 174,000 USD per year
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Job Details
- Required Skills
- PythonSQLData modeling
Requirements
- Deep experience architecting and owning production data platforms or distributed data systems.
- Strong Python, SQL, data modeling, ETL/ELT, workflow orchestration, and cloud-data-platform experience.
- Significant experience with unstructured data or document-processing systems at scale.
- Strong understanding of metadata, schema evolution, lineage, provenance, data quality, observability, retries, replay, backfills, and recovery.
- Experience designing systems with sensitive-data controls, governance, access management, and audit requirements.
- Strong understanding of how modern AI/ML and retrieval systems depend on production data architecture.
- Demonstrated technical judgment, ownership, problem solving, and ability to challenge fragile or overly complex designs.
- Strong technical communication and decision-making skills.
Responsibilities
- Architect large-scale data and document-processing platforms, from ingestion through downstream consumption.
- Design robust patterns for document parsing, extraction, normalization, validation, storage, search, human review, and reprocessing.
- Establish standards for schemas, metadata, lineage, provenance, data quality, reproducibility, and auditability.
- Design reliable ingestion and transformation pipelines using APIs, files, batch processing, and streaming where appropriate.
- Create platform patterns that enable Data Science and AI prototypes to become secure, maintainable production solutions.
- Architect data and retrieval foundations supporting RAG, analytics, AI workflows, evaluation, and feedback loops.
- Make architecture decisions involving scalability, reliability, security, privacy, cost, and operational complexity.
- Lead technical design reviews, investigate production failures, mentor engineers, and work across Data Engineering, Data Science, DevOps/MLOps, Product, Security, and client teams.
- Review and develop Python, SQL, schemas, APIs, and production data pipelines.
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