AI Engineer

Amentum; United States; Remote; USD 195,000 - 250,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Educational reimbursement

Parental leave

PTO and holidays

Stock options

Job summary

Amentum is seeking an AI Engineer to design, build, and deploy AI capabilities for secure use in classified and disconnected customer environments. The role emphasizes retrieval-augmented generation (RAG) and fine-tuning open-weight language models, including hosting models on Government infrastructure.

You will design, build, and deploy AI capabilities that operate inside classified environments, support customers and internal teams, and ensure responsible adoption of the technology.

Qualifications

• Bachelor's degree or equivalent in a technical field with minimum 5 years in software/machine learning/data engineering.
• Experience designing and deploying LLM/AI applications used by real users.
• Proficiency with RAG, fine-tuning, and open-weight LLMs in government-classified environments.

Responsibilities

• Design, build, and deploy AI systems integrated into production software.
• Implement RAG patterns, embeddings, vector stores, and retrieval evaluation.
• Fine-tune or adapt open-weight LLMs and benchmark results.
• Host and serve models on GPU infrastructure with appropriate sizing and quantization.
• Ensure security, compliance (RMF, ATO, ICD 503) and data handling for classified data.
• Collaborate with engineering, security, and mission teams to meet requirements.

Skills

Large language models

RAG patterns

Fine-tuning LLMs

Python

LangChain

LlamaIndex

Semantic Kernel

Docker

Kubernetes

GPU hosting

Security in classified envs

ATO/NIST RMF

VLLM/TGI/NVIDIA Triton

MLOps/LLMOps

Model evaluation

Document ingestion

Education

Bachelor's degree in CS/Engineering/Data Science/Math

Equivalent combination of education and experience

Tools

LangChain

LlamaIndex

Semantic Kernel

Docker

Kubernetes

vLLM

TGI

NVIDIA Triton

Job description

Amentum is seeking an AI Engineer to design, build, and deploy AI capabilities for secure use in classified and disconnected customer environments. The work emphasizes retrieval-augmented generation (RAG) and fine-tuning open-weight language models, including hosting models on Government infrastructure.

Role Responsibilities

• Design, build, and deploy versatile AI systems by integrating machine learning models into production software.


• Connect data science approaches with traditional software engineering, covering data preprocessing, model selection, API development, and scalable cloud infrastructure.


• Design, build, and deploy AI capabilities that operate inside classified and disconnected customer environments.


• Support customers and internal teams in understanding what the technology can deliver and how to adopt it responsibly.

Key Requirements

• Strong fundamentals in large language models, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection.


• Experience applying retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation.


• Knowledge of model adaptation methods such as supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, including knowing when each approach can outperform prompting or RAG alone.


• Familiarity with the open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar), plus experience navigating licensing, provenance, and supply chain considerations for Government deployments.


• Experience with security and compliance frameworks for classified environments, including RMF and the ATO process, ICD 503, the NIST AI Risk Management Framework, and handling requirements for CUI and classified data.


• Demonstrated experience designing, building, and deploying at least one RAG application for real users, including document ingestion, chunking, embedding selection, vector store configuration, and retrieval evaluation.


• Demonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and evaluating results against a defined benchmark.


• Experience hosting and serving models directly on GPU infrastructure (vLLM, TGI, NVIDIA Triton, or similar), including GPU sizing and quantization tradeoffs.


• Experience delivering AI in disconnected or classified environments, including moving models, containers, and dependencies across an air gap and supporting the system through ATO.


• Experience building access controls retrieval pipelines so users retrieve only content aligned to their clearance, compartments, and need-to-know.


• Experience building LLM evaluation and guardrails, including accuracy testing, hallucination measurement, red teaming, and prompt injection defense.


• Experience building document ingestion pipelines for large, unstructured collections, including parsing, OCR, metadata tagging, and preserving classification markings.


• Proficiency in Python and at least one LLM orchestration framework (LangChain, LlamaIndex, Semantic Kernel, or similar), plus containerization with Docker and Kubernetes.


• Ability to explain AI concepts, tradeoffs, and limitations clearly to technical and nontechnical audiences, including Government leadership.


• Ability to translate mission requirements into working AI architecture and select the right approach (prompting, RAG, fine-tuning, or agents) for each problem.


• Ability to evaluate emerging models and tools quickly and recommend adoption within customer security constraints.


• Ability to lead technical work across engineering, security, and mission teams.


• Ability to balance delivery speed with customer security, compliance, and governance requirements.

Security Clearance

• Active TS/SCI with Polygraph.

Education and Experience

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.


• We consider an equivalent combination of education and relevant experience in place of a degree.


• Minimum 5 years of professional experience in software engineering, machine learning engineering, or data engineering.


• At least 2 years designing, building, and deploying LLM or machine learning applications that real operational or business users rely on.

Technologies

• Python, LangChain, LlamaIndex, Semantic Kernel, Docker, Kubernetes


• vLLM, TGI, NVIDIA Triton


• LoRA, QLoRA, transformer architecture, RAG


• RMF, ATO process, ICD 503, NIST AI Risk Management Framework

Location and Schedule

• Location: Remote (remote)


• Work schedule: 5 days a week, 8 hours a day

Work Environment, Physical Demands, and Mental Demands

• Hybrid or modern office environment equipped with high-performance computing infrastructure and advanced cloud collaboration tools.


• Cross-functional collaboration across technical development teams and corporate executives to align data strategies with business goals.


• Environment can be fast-paced and high-pressure, requiring constant adaptation to evolving technologies.

Salary

USD 195,000 - 250,000 per year

Benefits

• Health, dental, and vision insurance


• Paid time off and holidays


• Retirement benefits (including 401(k) matching)


• Educational reimbursement


• Parental leave


• Employee stock purchase plan


• Tax-saving options


• Disability and life insurance


• Pet insurance

Preferred Qualifications

• Prior roles as an AI Engineer, Machine Learning Engineer, Software Engineer, Data Engineer, or Solution Architect with direct build responsibility.


• Experience supporting Intelligence Community, DoD, or Federal customers.


• Experience deploying AI workloads in classified cloud regions (AWS Secret or Top Secret, Azure Government Secret or Top Secret) or on GPU clusters the Government owns and operates.


• Experience building agentic workflows that call tools, APIs, or other systems, including Model Context Protocol (MCP) or similar integration standards.


• Experience with MLOps or LLMOps practices, including model versioning, CI/CD for models, monitoring, and drift detection.


• Experience preparing or contributing to ATO packages for systems that include AI or machine learning components.


• Experience with enterprise AI platforms such as Microsoft 365 Copilot, Azure AI Services, or Amazon Bedrock in Government cloud regions.


• Experience coaching teams on effective AI use and building business cases that quantify productivity gains from AI adoption.