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# Cognitive AI Engineer SWE

- **Company:** BigBear.ai, Inc.
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Signals Intelligence, Computer Networks, Extract Transform Load (ETL), Relational Databases, Data Flow Control, Systems Analysis, Python (Programming Language), Data Processing, Computer Network Operations, Data Ingestion, Backend, Apache Kafka, Machine Learning Operations, Front End Software Development, Vertica, Data Pipelines
- **Published:** October 28, 2026
- **Apply:** https://www.dice.com/job-detail/890e0051-874b-437b-b309-3e36fdecf6f2

## About the Role

* Active TS/SCI clearance with polygraph.
 * Strong proficiency in Python.
 * Experience developing data pipelines, ETL workflows, or data processing applications.
 * Experience with Kafka and streaming analytics.
 * Working knowledge of relational databases.
 * Demonstrated ability to use AI/ML tools to accelerate data analysis and engineering solution development.
 * Ability to collaborate effectively across technical and mission-focused teams.
 * Bachelor's degree in a technical discipline, or additional relevant experience in lieu of a degree., * Level 0: Bachelor's degree in a technical discipline, or 4 additional years of relevant experience in lieu of a degree.
 * Level 1: 3+ years of experience and a bachelor's degree in a technical discipline, or 4 additional years of relevant experience in lieu of a degree.
 * Level 2: 8+ years of experience and a bachelor's degree, or 4 additional years of relevant experience in lieu of a degree.
 * Level 3: 12+ years of experience and a bachelor's degree, or 10+ years of experience and a master's degree.

What we'd like you to have

 * Hands-on experience supporting production cyber network operations capabilities.
 * Experience analyzing cybersecurity datasets, including network traffic and operating system events.
 * Understanding of end-to-end SIGINT collection and analysis systems.
 * Familiarity with ClickHouse.
 * Exposure to related domains such as dataflow, data science, mission analytics, or operational technology environments.

## Description

BigBear.ai is seeking a talented engineers to join a high-impact team delivering mission-critical infrastructure awareness and analytics. In this role, you will help aggregate and reconcile data from multiple sources to provide a clear, actionable view of what infrastructure was requested, what was ultimately deployed, and where discrepancies may exist.

Your work will directly support subject matter experts as they investigate infrastructure decisions, develop best practices, and refine deployment rules. You will help identify and surface situations where requested or provisioned infrastructure does not align with established standard enabling stakeholders to respond quickly and make better-informed decisions.

This is an opportunity to own end-to-end feature delivery, from concept through production, while transforming complex data into actionable insights and using AI-enabled tools to accelerate analysis, prototyping, and solution development. You will collaborate with stakeholders across the organization and work in an iterative, mission-focused environment where your contributions have meaningful operational impact.

What you will do

 * Design, develop, and maintain full-stack applications across frontend and backend systems.
 * Build and maintain ETL workflows that reconcile requested infrastructure with infrastructure actually deployed.
 * Develop scalable, reliable data ingestion and processing capabilities.
 * Leverage AI and machine learning tools to accelerate data analysis, prototype solutions, and improve engineering workflows.
 * Integrate data products and analytical results into user-focused applications and visualizations.
 * Collaborate with full-stack engineers, data specialists, subject matter experts, and organizational stakeholders.
 * Help identify discrepancies, policy violations, and deviations from approved infrastructure best practices.
 * Drive features from initial concept through implementation, testing, deployment, and continued improvement.