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Swingtech•Washington, DC

1d•Hybrid

About The Position

The AI/ML Data Engineer develops and sustains the secure data pipelines, data products, retrieval foundations, and governance controls that enable DOL’s AI/ML solutions. The position supports structured, semi-structured, and unstructured data sources used for analytics, AI/ML development, document intelligence, RAG, and production AI applications.

Requirements

Bachelor’s degree in computer science, data engineering, data science, information systems, software engineering, mathematics, or a related technical discipline.
At least four years of experience in data engineering, database development, analytics engineering, ETL/ELT development, data-platform implementation, or related work.
Strong SQL and Python development skills.
Experience designing data pipelines and integrating APIs, databases, file systems, cloud storage, data warehouses, or data lakes.
Experience with data modeling, metadata, data quality, data lineage, data transformation, monitoring, and operational support.
Familiarity with AWS, Azure, Google Cloud, or equivalent cloud data services.
Knowledge of secure data-handling practices, including access control, encryption, data masking, PII protection, and logging.
Must be willing to work 3 days onsite at customer site in Washington, DC.

Nice To Haves

Experience with AWS Glue, S3, Athena, Redshift, Lake Formation, Azure Data Factory, Azure Data Lake Storage, Databricks, Snowflake, BigQuery, or equivalent platforms.
Experience with vector databases or vector-search capabilities, including OpenSearch, pgvector, Pinecone, Weaviate, Milvus, Chroma, FAISS, or similar tools.
Experience with RAG, document intelligence, OCR, enterprise search, knowledge management, document classification, or content-ingestion pipelines.
Familiarity with Federal data governance, FedRAMP, FISMA, NIST 800-53, NIST 800-171, CUI, Privacy Act, and records-management requirements.

Responsibilities

Design, build, test, deploy, and maintain scalable data pipelines for batch, streaming, near-real-time, and event-driven workloads.
Integrate approved agency data sources, APIs, file stores, document repositories, relational databases, data lakes, data warehouses, and authorized external sources.
Develop ETL/ELT pipelines for data extraction, validation, transformation, normalization, enrichment, de-identification, metadata management, and loading.
Implement document-ingestion pipelines that support OCR, parsing, classification, metadata extraction, PII detection/redaction, chunking, embeddings, vector indexing, and retrieval workflows.
Create and maintain data models, schemas, data dictionaries, metadata structures, catalog records, and data-quality controls.
Implement data lineage, source provenance, dataset versioning, retention, access controls, and auditability for training, validation, evaluation, and production datasets.
Preserve the separation of training, validation, and final evaluation datasets through controlled access, versioning, and documented lifecycle processes.
Develop and monitor data-quality measures, including completeness, accuracy, timeliness, duplication, validity, freshness, distribution drift, and labeling quality.
Apply data minimization, masking, encryption, access controls, de-identification, and least-privilege safeguards to PII, CUI, and other protected DOL data.
Collaborate with AI/ML Engineers to optimize retrieval quality, embeddings, vector stores, hybrid search, reranking, citation traceability, and knowledge-base refresh processes.
Develop data-pipeline runbooks, technical documentation, source inventories, lineage artifacts, data-quality reports, and operational support procedures.
Support security, privacy, ATO, Responsible AI, incident response, MLOps, monitoring, and release-readiness activities.