AI Engineer

Amentum is hiring an AI Engineer for Remote work, with a focus on designing, building, and deploying AI capabilities that can operate securely in classified and disconnected customer environments. The role emphasizes retrieval-augmented generation (RAG) and fine-tuning open-weight language models, including support for Government infrastructure and responsible adoption.

Key Responsibilities

• Design, build, and deploy AI systems by integrating machine learning models into production software.
• Connect data science and software engineering workstreams, covering data preprocessing, model selection, API development, and cloud infrastructure scalability.
• Design, build, and deploy AI capabilities that run inside classified and disconnected customer environments.
• Support customers and internal teams with guidance on what the technology can deliver and how to adopt it responsibly.

Core 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.
• Proficiency with retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation.
• Experience with model adaptation approaches such as supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, including knowing when each method outperforms prompting or RAG alone.
• Familiarity with open-weight model ecosystems (Llama, Mistral, Qwen, Gemma, or similar) and considerations for licensing, provenance, and supply chain when deploying in Government environments.
• Knowledge of 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 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 measuring outcomes against a defined benchmark.
• Experience hosting and serving models directly on GPU infrastructure, using tools such as vLLM, TGI, NVIDIA Triton, or similar, including GPU sizing and quantization tradeoffs.
• Experience delivering AI capabilities in disconnected or classified environments, including moving models, containers, and dependencies across the air gap and supporting the system through ATO.
• Experience building access controls retrieval pipelines that ensure users retrieve only content aligned with clearance, compartments, and need-to-know.
• Experience creating 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, at least one LLM orchestration framework (LangChain, LlamaIndex, Semantic Kernel, or similar), and containerization with Docker and Kubernetes.
• Ability to communicate AI concepts, tradeoffs, and limitations clearly to both technical and nontechnical audiences, including Government leadership.
• Ability to translate mission requirements into working AI architecture and select appropriate approaches (prompting, RAG, fine-tuning, or agents) for each problem.
• Ability to evaluate emerging models and tools and recommend adoption that fits 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.
• Active TS/SCI with Polygraph.

Education and Experience

• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field. Equivalent combinations of education and relevant experience may be considered.
• 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 used by real operational or business users.

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 including 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.

Technology Stack

• 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

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

Compensation

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