Large Language Model Specialist

Peraton; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Peraton is seeking an LLM Specialist to design and deploy advanced large language model capabilities within our cloud-based analytics ecosystem. You will own architecture for fine-tuning, RAG, and agentic frameworks, guiding prototypes and scalable pipelines.

Collaborate with engineering, product, and data teams, ensure governance, security, and performance, and mentor junior engineers while evaluating emerging LLM technologies and best practices.

Qualifications

• Extensive experience with LLM architectures and transformers.
• Proven track record in end-to-end RAG and retrieval systems.
• Experience leading fine-tuning and parameter-efficient training.
• Strong Python development skills and GPU/distributed training experience.
• Ability to govern ML pipelines with MLOps practices.

Responsibilities

• Lead design, development, and deployment of advanced LLM capabilities in a cloud analytics environment.
• Oversee fine-tuning, RAG, and agentic frameworks across projects.
• Develop scalable LLM pipelines with monitoring and governance.
• Collaborate with engineering, product, and data teams to drive deployment and adoption.

Skills

LLM architectures

Transformer models

Fine-tuning

RAG pipelines

Python development

Distributed compute

Mentoring/leadership

Cross-functional communication

Education

BS/BA in a relevant field

MS/MA in a relevant field

PhD

Tools

LangChain

LlamaIndex

Azure

AWS

GCP

Job description

Required Skills:

• Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
• Deep expertise in LLM architectures, transformer models, and modern generative AI techniques.
• Demonstrated experience leading fine‑tuning efforts, parameter‑efficient training, and advanced prompt engineering.
• Proven ability to design and implement end‑to‑end RAG pipelines, including embedding workflows, retrieval optimization, and vector database integrations.
• Hands‑on experience with one or more LLM frameworks or orchestration toolchains (such as LangChain, LlamaIndex).
• Strong Python development skills and experience with distributed compute or GPU‑accelerated training environments.
• Experience architecting and deploying AI/ML or LLM workflows within cloud platforms such as Azure, AWS, or GCP.
• Solid understanding of MLOps/LLMOps practices, including versioning, CI/CD, automated testing, monitoring, and model governance.
• Ability to lead technical discussions, mentor team members, and communicate complex AI concepts to diverse audiences.
• US Citizen with the ability to obtain/maintain a Public Trust clearance

Preferred Skills:

• Experience implementing multi‑agent or agentic AI systems for task automation and reasoning.
• Familiarity with LLM evaluation frameworks, structured benchmarking, or human‑in‑the‑loop refinement methods (e.g., RLHF‑style workflows).
• Expertise with advanced retrieval techniques such as hybrid search, graph retrieval, or long‑context optimization.
• Experience optimizing model inference through quantization, model compression, or model distillation.
• Background integrating LLM services with large‑scale analytics environments (e.g., Databricks, Snowflake, Spark).
• Strong skills in exploratory data analysis, feature engineering, and data modeling to support domain‑specific LLM customization.
• Experience developing innovative prototypes or POCs that leverage state‑of‑the‑art generative AI approaches.
• Exposure to emerging architectures such as mixture‑of‑experts models, long‑context transformers, or experimental generative frameworks.

The LLM Specialist will drive the design, development, and operationalization of advanced large‑language‑model capabilities across a cloud‑based analytics ecosystem. This role leads innovation efforts around cutting‑edge AI, owning the architecture and strategy for fine‑tuning, retrieval‑augmented generation (RAG), agentic frameworks, and domain‑specific model adaptation. The specialist will guide the development of high‑impact prototypes, oversee the evolution of scalable LLM pipelines, and ensure robust governance, security, and performance across all model implementations. Partnering with engineering, product, and data teams, this position provides technical leadership, evaluates emerging LLM technologies, sets best practices, and helps drive transformation through the practical, safe, and effective deployment of generative AI.