AI/ML Data Engineer

The AI/ML Data Engineer will develop and sustain secure data pipelines, data products, retrieval foundations, and governance controls to support the Department of Labor (DOL) AI and ML solutions. This hybrid role supports structured, semi-structured, and unstructured data across analytics, AI/ML development, document intelligence, RAG, and production AI applications.

Key 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, including APIs, file stores, document repositories, relational databases, data lakes, data warehouses, and authorized external sources.
• Develop ETL/ELT pipelines for extraction, validation, transformation, normalization, enrichment, de-identification, metadata management, and loading.
• Implement document-ingestion pipelines supporting 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 for 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 and technical documentation, including 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.

Required Qualifications

• 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.
• Willingness to work 3 days onsite at a customer site in Washington, DC.

Technologies

• SQL, Python
• AWS, Azure, Google Cloud
• AWS Glue, S3, Athena, Redshift, Lake Formation
• Azure Data Factory, Azure Data Lake Storage, Databricks
• Snowflake, BigQuery
• Vector databases and vector-search tooling: OpenSearch, pgvector, Pinecone, Weaviate, Milvus, Chroma, FAISS
• RAG, OCR
• Compliance and security: FISMA, FedRAMP, NIST 800-53, NIST 800-171

Preferred Qualifications

• 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.

Benefits

• 15 PTO days
• 11 paid holidays
• Medical Insurance with 3 options (HSA with $600 Employer Contribution)
• Dental Insurance with no age limit orthodonture
• Vision Insurance through EyeMed in and out of network coverage
• Short Term and Long-Term Disability coverage with 100% premium support
• Life insurance and AD&D with 100% premium support
• Supplemental Life Insurance
• Critical Care and Accident Insurance availability
• Pet Insurance through Nationwide
• Employee Assistance Program
• 401k with enrollment from day one; 4% deferral by company
• $1500 Annual Training Budget
• $1500 Referral bonus
• Eligibility for annual merit and discretionary bonus
• Flexible work arrangements