Principal AI/ML Engineer (Large Language Model)

CACI International Inc; Aurora (CO); On-site; USD 150,000 - 230,000; Full time

Job summary

CACI International Inc is seeking a Principal AI/ML Engineer (Large Language Model) to advance AI/ML algorithms across domains such as object detection, NLP, reinforcement learning, and large language models.

You will lead and mentor a multidisciplinary team of developers and researchers, apply LLMs to remote sensing applications, fine‑tune foundation models, build adaptors, and implement RAG techniques with vector databases like Weaviate.

Qualifications

• Experience leading ML teams
• Experience applying LLMs
• Remote sensing ML experience

Responsibilities

• Lead and mentor a multidisciplined team of developers and researchers.
• Apply Large Language Models to remote sensing tasks.
• Fine-tune foundation models and build adaptors for new applications (llama factory, PEFT).
• Apply retrieval augmented generation (RAG) techniques to data and query vector databases (Weaviate).
• Build custom applications with ML solutions.

Skills

Leadership

ML engineering

NLP

Reinforcement learning

Large Language Models

Vector databases

Tools

Weaviate

Llama factory

PEFT

Job description

Job Title: Principal AI/ML Engineer (Large Language Model)

Job Category: Science

Time Type: Full time

Minimum Clearance Required to Start: TS/SCI with Polygraph

Employee Type: Regular

Percentage of Travel Required: Up to 10%

Type of Travel: Local

Anticipated Posting End: 12/31/2026

The Opportunity

The Principal AI/ML Engineer will support the development of AI/ML algorithms in a multitude of disciplines from object detection/classification, natural language processing, reinforcement learning, and large language models.

Responsibilities

• Lead and mentor a multidisciplined team consisting of developers and researchers to implement machine learning algorithms to solve a broad set of challenges for our various customers
• Apply Large Language Models (LLMs) to a variety of applications within remote sensing such as tasking collections, identifying gaps in collection plans, analyzing patterns of life, and more.
• Fine tune foundation models and building adaptors for new applications (llama factory, PEFT)
• Apply retrieval augmented generation (RAG) techniques to data to populate and query vector databases (e.g. Weaviate)
• Build custom applications with