Qualifications
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.
Target Salary Range $104,000 - $166,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Benefits Statement
Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
Application Statements The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on-camera interview, as well as participate in a process to verify your identity. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.
- EEO
- EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
