Secure Federal MLOps Engineer (On-Prem)

Analytica

Bethesda (MD)

On-site

USD 160,000 - 190,000

Full time

2 days ago

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Job summary

Analytica in Bethesda, MD seeks an experienced MLOps Engineer to automate secure deployment, evaluation, monitoring, and lifecycle management for on-prem AI services as part of a Federal data analytics initiative.

The team will deliver a secure, scalable platform that integrates structured and unstructured data, provides data visualization and traceable AI-assisted analytics, and offers examiners centralized tools for search, review, monitoring, and decision support.

Qualifications

Building, integrating, and operating data pipelines for ML, NLP, or analytics.
Feature engineering, document processing, embeddings, curated datasets.
Proficiency in Python and SQL for data engineering, automation, and model support.
Experience with structured and unstructured data prep for AI/ML or analytics applications.
Data quality controls, provenance, metadata, versioning, lineage, and traceability for AI/ML systems.
Clear technical documentation covering pipelines, schemas, transformations, dependencies, and operations.
Strong communication, problem-solving, and cross-functional collaboration skills.
U.S. citizenship and ability to obtain and maintain Top Secret eligibility.
Active Top Secret clearance highly preferred.

Responsibilities

Design, develop, and maintain data pipelines and ETL/ELT workflows for ML, NLP, retrieval, and analytics.
Prepare, transform, curate, and validate structured and unstructured data for AI/ML apps.
Perform feature engineering, document processing, embeddings generation, and dataset preparation.
Develop reproducible data transformations and workflows using Python, SQL, and related data engineering tech.
Implement data quality, validation, provenance, metadata, versioning, and traceability across the data lifecycle.
Prepare and integrate data for search, Retrieval-Augmented Generation (RAG), visualization, analytics, and AI-assisted decision support.
Troubleshoot data pipeline, transformation, integration, and data-quality issues across environments.
Collaborate with data engineering, data science, software engineering, and AI/ML teams during development and testing.
Develop technical documentation covering data architecture, pipelines, schemas, dependencies, and operational procedures.
Support deployment and operation of data engineering capabilities within secure Federal, on-premises, cloud, or hybrid environments.
Ensure compliance with Federal security, privacy, accessibility, records-management, data-ownership, and governance requirements.