AI/ML Data Engineer — Scalable Pipelines & RAG

RiseMe; Washington (District of Columbia); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

15 PTO days

11 paid holidays

Medical insurance with HSA option

Job summary

RiseMe is seeking an AI/ML Data Engineer to design, build, and sustain secure data pipelines and governance for analytics and production AI applications. You will work with structured, semi-structured, and unstructured data, enabling retrieval, embeddings, and RAG workflows across cloud data stores.

The role requires 4+ years in data engineering, strong SQL/Python, and on-site presence in Washington, DC. You will implement data models, lineage, masking, and access controls while collaborating

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.
• Must be willing to work 3 days onsite at customer site in Washington, DC.

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.

Skills

SQL

Python

Education

Bachelor's degree in computer science or related field

Tools

AWS

Azure

Google Cloud

Job description

RiseMe is seeking an AI/ML Data Engineer to design, build, and sustain secure data pipelines and governance for analytics and production AI applications. You will work with structured, semi-structured, and unstructured data, enabling retrieval, embeddings, and RAG workflows across cloud data stores.

The role requires 4+ years in data engineering, strong SQL/Python, and on-site presence in Washington, DC. You will implement data models, lineage, masking, and access controls while collaborating