MLOps Engineer

Analytica•Bethesda, MD

7d•Onsite

About The Position

We are seeking an experienced MLOps Engineer to automate secure deployment, evaluation, monitoring, and lifecycle management for on-premises AI services for a Federal data analytics and AI modernization initiative. The team will deliver a secure, scalable platform that integrates structured and unstructured data, provides data visualization and traceable AI-assisted analytics, and gives examiners centralized tools for search, review, monitoring, and decision support. The platform will support professional judgment and will not replace authoritative agency financial or grants-management systems or execute financial transactions. Analytica has been recognized by Inc. Magazine as the fastest-growing private US small business. We work with U.S. government customers in health, civilian, and national security missions. As a core member you’ll work with a diverse team of professionals to solve matters, architect nuisances, and come up with alternatives.

Requirements

Demonstrated experience building, integrating, and operating data pipelines for machine learning, NLP, information retrieval, or advanced analytics.
Hands-on experience with feature engineering, document processing, embeddings, curated datasets, data transformation, and reproducible data workflows.
Strong proficiency in Python and SQL for data engineering, data transformation, automation, and model-support workflows.
Experience working with structured and unstructured data and preparing data for AI/ML or analytics applications.
Experience implementing data quality controls, data validation, provenance, metadata, versioning, lineage, and source traceability for AI/ML or data-intensive systems.
Experience troubleshooting and optimizing data pipelines, integrations, transformations, and data-processing workflows.
Ability to develop clear technical documentation covering data pipelines, schemas, transformations, dependencies, and operational processes.
Strong technical communication, problem-solving, and cross-functional collaboration skills.
U.S. citizenship and ability to obtain and maintain Top Secret eligibility, as required for contractor personnel supporting the effort.
Active Top Secret clearance highly preferred.

Nice To Haves

Experience supporting Federal data, financial management, budget execution, grants management, payment systems, award oversight, financial reporting, or financial stewardship data.
Experience delivering data engineering or AI/ML solutions in secure Federal, on-premises, cloud, or hybrid environments.
Experience with Linux-based environments.
Experience with PostgreSQL and/or Microsoft SQL Server.
Experience working with Java and/or .NET applications and integrations.
Familiarity with Jenkins and CI/CD pipelines.
Experience with self-hosted Azure DevOps.
Experience with Kubernetes and/or Rancher.
Familiarity with open-source AI/ML models and supporting infrastructure.
Experience supporting RAG, vector search, semantic search, embeddings pipelines, or other AI retrieval architectures.
Familiarity with Federal data governance, security, privacy, and compliance requirements.

Responsibilities

Design, develop, and maintain data pipelines and ETL/ELT workflows supporting machine learning, NLP, retrieval, and advanced analytics.
Prepare, transform, curate, and validate structured and unstructured grants and financial data for AI/ML applications.
Perform feature engineering, document processing, chunking, embeddings generation, and dataset preparation for AI-assisted analytics and retrieval workflows.
Develop reproducible data transformations and workflows using Python, SQL, and applicable data engineering technologies.
Implement and maintain data quality, validation, provenance, lineage, versioning, metadata, and source traceability throughout 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 development and testing environments.
Collaborate with multidisciplinary data engineering, data science, software engineering, and AI/ML teams during iterative development and testing.
Develop technical documentation covering data architecture, pipelines, transformations, schemas, features, embeddings, dependencies, and operational procedures.
Support deployment and operation of data engineering capabilities within secure Federal, on-premises, cloud, or hybrid environments.
Ensure data engineering solutions comply with Federal security, privacy, accessibility, records-management, data-ownership, and governance requirements.