Senior AI Engineer

Talentify; Maryland; On-site; USD 170,000 - 210,000; Full time

Job summary

BigBear.ai is seeking a Senior AI Engineer to design, develop, and maintain LLM orchestration frameworks coordinating static analyzers, RAG systems, and automated testing pipelines. You will build data preprocessing pipelines to ingest and vectorize documents and code for LLM consumption, and design databases to support RAG architectures, storage, audit trails, and metadata management.

You will collaborate with leadership and mission stakeholders to ensure scalable, secure AI tooling, perform

Qualifications

• Bachelor's degree plus 8 years of relevant experience or equivalent.
• Active TS/SCI with Poly.
• Proficiency in Python, LLM integration, and data engineering.
• Experience with static analyzers, RAG framework, and relational and vector databases. Familiarization with Agile, Git, Jira, and Confluence.
• Collaboratively work with systems engineers to architect and deploy networked AI tools within secure enclaves and conduct performance profiling, optimization, and scalability analysis of mission systems.
• Support security assessments and authority to operate (ATO) processes.

Responsibilities

• Design, develop, and maintain LLM orchestration frameworks coordinating static analyzers, RAG systems, and automated testing pipelines.
• Develop and optimize data preprocessing pipelines to ingest, clean, transform, and vectorize product documentation, source code, and test results for LLM consumption.
• Design and implement database solutions to support RAG architectures, artifact storage, audit trails, and metadata management.
• Collaborate with technical leadership and mission stakeholders to capture requirements and improve certification workflows.
• Conduct performance profiling, optimization, and scalability analysis of mission systems and AI tools.

Skills

Python

LLM integration

Data engineering

Static analyzers

RAG framework

Relational databases

Vector databases

Agile methodologies

Git

Jira

Confluence

Active TS/SCI with Poly

Collaboration with systems engineers

Education

Bachelor's degree + 8 years experience

Tools

Git

Jira

Confluence

Job description

Residency

All applicants must currently reside in the United States

Overview

BigBear.ai is seeking a motivated Senior AI Engineer with a proven track record in software engineering and deep understanding of Artificial Intelligence/Machine Learning (AI/ML) techniques to support the exciting new AI and Emerging Technologies (AI&ET) mission. The engineer will work closely with mission stakeholders to capture requirements, aid in structured planning, and improve the certification process by introducing AI/ML tooling. Additionally, the engineer will use AI/ML to develop tools and techniques to include fine tuning Large Language Models (LLM) that assist in enhanced vulnerability identification and mitigation capabilities.

What you will do

Collaborate closely with technical leadership to design, develop, and maintain LLM orchestration frameworks that coordinate static analyzers, retrieval-augmented generation (RAG) systems, and automated testing pipelines. Develop and optimize data preprocessing pipelines to ingest, clean, transform, and vectorize product documentation, source code, and test results for LLM consumption. Design and implement database solutions to support RAG architectures, artifact storage, audit trails, and metadata management.

What you need to have

• Bachelor's degree plus 8-years of relevant experience or equivalent.
• Active TS/SCI with Poly
• Proficiency in Python, LLM integration, and data engineering.
• Experience with static analyzers, RAG framework, and relational and vector databases. Fimiliarization with Agile, Git, Jira, and Confluence.
• Collaboratively work with systems engineers to architect and deploy networked AI tools within secure enclaves and conduct performance profiling, optimization, and scalability analysis of mission systems.
• Support security assessments and authority to operate (ATO) processes.

What we'd like you to have

• Familiarization with RUST, Claude Code, Codex, and CI/CD.

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.

• Bachelor's degree plus 8-years of relevant experience or equivalent.
• Active TS/SCI with Poly
• Proficiency in Python, LLM integration, and data engineering.
• Experience with static analyzers, RAG framework, and relational and vector databases. Fimiliarization with Agile, Git, Jira, and Confluence.
• Collaboratively work with systems engineers to architect and deploy networked AI tools within secure enclaves and conduct performance profiling, optimization, and scalability analysis of mission systems.
• Support security assessments and authority to operate (ATO) processes.

Collaborate closely with technical leadership to design, develop, and maintain LLM orchestration frameworks that coordinate static analyzers, retrieval-augmented generation (RAG) systems, and automated testing pipelines. Develop and optimize data preprocessing pipelines to ingest, clean, transform, and vectorize product documentation, source code, and test results for LLM consumption. Design and implement database solutions to support RAG architectures, artifact storage, audit trails, and metadata management.