AI/ML Data Engineer

Bigbear.ai•Reston, VA

5d•Remote

About The Position

BigBear.ai is seeking an AI/ML Data Engineer to architect, build, and operate enterprise-scale AI data platforms enabling vector databases, semantic search, Retrieval-Augmented Generation (RAG), and agentic AI systems. This role will establish the technical and governance foundations for production AI, including data lineage, source attribution, prompt/context traceability, explainability, and evaluation in mission environments.

Requirements

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.
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.
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.

Nice To Haves

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.

Responsibilities

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, and 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.