What you need to have
Active Top Secret / SCI with Polygraph is mandatory.
Bachelor's in CS/Engineering/Math/Data Science (or equivalent experience).
20+ years in software engineering, data engineering, distributed systems, cloud architecture, or AI/ML platform development.
Proven delivery of enterprise-scale AI/ML / GenAI / agentic systems.
Track record of architecting production cloud-native data platforms supporting AI workloads.
Experience in complex enterprises with security constraints, dependencies, governance, and competing priorities.
Deep experience with data pipelines supporting ML models, vector databases, semantic search, and GenAI apps.
End-to-end delivery ownership from strategic requirements through operational deployment.
Technical
Expert in Python and SQL; strong software engineering practices.
Deep experience with AWS, Azure, or GCP data/AI platforms.
Strong understanding of distributed systems, cloud-native architecture, MLOps, platform engineering.
Hands-on with vector DBs, embeddings, retrieval systems, RAG.
Experience with CI/CD, orchestration, IaC/automation, observability.
Leadership & Communication
Exceptional written/verbal communication.
Able to translate complex AI/data architecture concepts into mission impact, risk, and tradeoffs.
Demonstrated ability to influence and drive consensus across stakeholders.
What we'd like you to have
Prior roles as Principal Engineer, Lead Data Engineer, Solutions Architect, Technical Lead.
Built/operated enterprise vector search/knowledge management / RAG / LLM platforms.
Large-scale distributed processing (e.g., Spark/Flink/Beam—whatever aligns to your stack).
Experience supporting AI adoption in government/defense/intelligence or highly regulated environments.
Familiarity with AI governance, evaluation frameworks, explainability, responsible AI.
Pay transparency
Please note the targeted compensation range is provided as an estimate, and any actual compensation offer may vary depending on the needs of the company, or an applicant's skillset, competencies, experience, education, certifications, location, or other factors. The estimated range does not include the value of any benefits offered.
About BigBear.ai
BigBear.ai is a leading provider of AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai’s predictive analytics capabilities in highly complex, distributed, mission-based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit https://bigbear.ai/ and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai.
BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.
What You’ll Do
Architect and evolve an enterprise AI data platform enabling vector search, semantic retrieval, RAG, and agentic workflows.
Design cloud-native, distributed data systems optimized for performance, scale, security, reliability, and cost.
Establish and implement controls for
Data quality, lineage, provenance/source attribution
Prompt + context traceability and auditability
Explainability and evaluation of AI outputs
Partner across Data Science, ML Engineering, Software, Cybersecurity, and Enterprise Architecture to translate AI requirements into production-grade capabilities.
Lead architectural decisions; drive reuse across organizations and eliminate duplication. Implement monitoring/observability/alerting and operational excellence best practices.
Mentor engineers and raise engineering standards (design reviews, coding standards, CI/CD discipline).
Evaluate emerging AI technologies (vector DBs, retrieval frameworks, evaluation stacks) and recommend adoption paths.
Active Top Secret / SCI with Polygraph is mandatory.
Bachelor's in CS/Engineering/Math/Data Science (or equivalent experience).
20+ years in software engineering, data engineering, distributed systems, cloud architecture, or AI/ML platform development.
Proven delivery of enterprise-scale AI/ML / GenAI / agentic systems.
Track record of architecting production cloud-native data platforms supporting AI workloads.
Experience in complex enterprises with security constraints, dependencies, governance, and competing priorities.
Deep experience with data pipelines supporting ML models, vector databases, semantic search, and GenAI apps.
End-to-end delivery ownership from strategic requirements through operational deployment.
Technical
Expert in Python and SQL; strong software engineering practices.
Deep experience with AWS, Azure, or GCP data/AI platforms.
Strong understanding of distributed systems, cloud-native architecture, MLOps, platform engineering.
Hands-on with vector DBs, embeddings, retrieval systems, RAG.
Experience with CI/CD, orchestration, IaC/automation, observability.
Leadership & Communication
Exceptional written/verbal communication.
Able to translate complex AI/data architecture concepts into mission impact, risk, and tradeoffs.
Demonstrated ability to influence and drive consensus across stakeholders.
