Large Language Model Specialist – AWS Cloud

Peraton

. Drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem .

Posted 9/21/2026full-timeUnited StatesMid-LevelSenior💰 $86,000 - $138,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in LLM architectures and modern generative AI techniques, with a strong focus on designing and implementing end-to-end RAG pipelines and scalable LLM workflows. Proven ability to lead technical discussions and mentor team members while ensuring robust governance and performance across model implementations.

Highest-signal resume keywords

LLM Architecture ExpertiseFine-Tuning and Prompt EngineeringEnd-to-End RAG Pipeline DesignPython Development SkillsMLOps/LLMOps Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM ArchitecturesTransformer ModelsGenerative AI TechniquesFine-TuningParameter-Efficient TrainingRAG Pipeline ImplementationPython DevelopmentDistributed ComputeMLOps PracticesModel Governance

Soft Skills

Technical LeadershipMentoringComplex Concept Communication

Tools & Technologies

LangChainLlamaIndexAzureAWSGCPDatabricksSnowflakeSpark

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

AIMLGenerative AIModel Inference OptimizationHybrid SearchGraph RetrievalLong-Context Optimization

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSpark

About the role

Key responsibilities & impact

Drive the design, development, and operationalization of advanced large-language-model capabilities across a cloud-based analytics ecosystem
Lead innovation efforts around cutting-edge AI
Own architecture and strategy for fine-tuning, retrieval-augmented generation (RAG), agentic frameworks, and domain-specific model adaptation
Guide development of high-impact prototypes
Oversee evolution of scalable LLM pipelines
Ensure robust governance, security, and performance across model implementations
Partner with engineering, product, and data teams
Provide technical leadership and evaluate emerging LLM technologies
Set best practices and help drive transformation through practical, safe, and effective deployment of generative AI

Requirements

What you’ll need

5 years with BS/BA; 3 years with MS/MA; 0 years with PhD
Deep expertise in LLM architectures, transformer models, and modern generative AI techniques
Demonstrated experience leading fine-tuning efforts, parameter-efficient training (e.g., LoRA/PEFT), 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: experience implementing multi-agent or agentic AI systems for task automation and reasoning
Preferred: familiarity with LLM evaluation frameworks, structured benchmarking, or human-in-the-loop refinement methods (e.g., RLHF-style workflows)
Preferred: expertise with advanced retrieval techniques such as hybrid search, graph retrieval, or long-context optimization
Preferred: experience optimizing model inference through quantization, model compression, or model distillation
Preferred: background integrating LLM services with large-scale analytics environments (e.g., Databricks, Snowflake, Spark)
Preferred: strong skills in exploratory data analysis, feature engineering, and data modeling to support domain-specific LLM customization
Preferred: experience developing innovative prototypes or POCs that leverage state-of-the-art generative AI approaches
Preferred: exposure to emerging architectures such as mixture-of-experts models, long-context transformers, or experimental generative frameworks

Benefits

Comp & perks

Discretionary bonus may be available in addition to base pay
Overtime may be available
Shift differential may be available