{"id":2400467,"url":"https://alion.io/job/amentum-ai-engineer","title":"AI Engineer","company":{"id":14861,"name":"Amentum","domain":"amentum.com","url":"https://alion.io/company/amentum","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hanover, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":195000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Gemma","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"Model Distillation","optional":false},{"name":"NIST AI RMF","optional":false},{"name":"OCR","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Red Teaming","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SFT","optional":false},{"name":"TGI","optional":false},{"name":"Tokenization","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Copilot","optional":true},{"name":"LLMOps","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-10-12T23:25:55Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T04:44:56Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Amentum is hiring an AI Engineer in Hanover, MD responsible for designing, building, and deploying AI capabilities that run inside classified and disconnected customer environments. The work centers on retrieval-augmented generation (RAG), fine-tuning and adapting open-weight language models, and serving those models securely on Government infrastructure. The engineer also helps customers and internal teams understand what technology can deliver and how to adopt it responsibly.\nWork Schedule:\n5 days a week, 8 hours a day\nEssential Responsibilities:\nResponsible for designing, building, and deploying versatile artificial intelligence systems by integrating machine learning models into production software. They bridge the gap between data science and traditional software engineering, handling everything from data preprocessing and model selection to API development and cloud infrastructure scalability.\nWork Environment, Physical Demands, and Mental Demands:\nHybrid or modern office environment equipped with high-performance computing infrastructure and advanced cloud collaboration tools. Their day-to-day involves cross-functional collaboration, straddling the line between technical development teams and corporate executives to align data strategies with business goals. The environment can be fast-paced and high-pressure, requiring constant adaptation to evolving technologies.\nMinimum Knowledge\nLarge language model fundamentals, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection.\nRetrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation.\nModel adaptation methods, including supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, along with when each approach outperforms prompting or RAG alone.\nThe open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar) and the licensing, provenance, and supply chain considerations for bringing models into Government environments.\nSecurity and compliance frameworks that govern AI in classified environments, including RMF and the ATO process, ICD 503, the NIST AI Risk Management Framework, and handling requirements for CUI and classified data.\nMinimum Skills\nDemonstrated experience designing, building, and deploying at least one RAG application for real users, covering document ingestion, chunking, embedding selection, vector store configuration, and retrieval evaluation.\nDemonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and measuring results against a defined benchmark.\nExperience hosting and serving models directly on GPU infrastructure (vLLM, TGI, NVIDIA Triton, or similar), including GPU sizing and quantization tradeoffs.\nExperience delivering AI capabilities in disconnected or classified environments, including moving models, containers, and dependencies across the air gap and supporting the system through ATO.\nExperience building access controls retrieval pipelines so each user retrieves only content that matches their clearance, compartments, and need-to-know.\nExperience building evaluation and guardrails for LLM output, including accuracy testing, hallucination measurement, red teaming, and prompt injection defense.\nExperience building document ingestion pipelines for large, unstructured collections, including parsing, OCR, metadata tagging, and preserving classification markings.\nProficiency in Python and at least one LLM orchestration framework (LangChain, LlamaIndex, Semantic Kernel, or similar), plus containerization with Docker and Kubernetes.\nAbility to explain AI concepts, tradeoffs, and limitations clearly to technical and nontechnical audiences, including Government leadership.\nMinimum Abilities\nAbility to translate mission requirements into working AI architecture and choose the right approach (prompting, RAG, fine-tuning, or agents) for each problem.\nAbility to evaluate emerging models and tools quickly and recommend which merit adoption within customer security constraints.\nAbility to lead technical work across engineering, security, and mission teams.\nAbility to balance delivery speed with customer security, compliance, and governance requirements.\nSecurity Clearance Required:\nActive TS/SCI with Polygraph.\nMinimum Education:\nBachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.\nWe consider an equivalent combination of education and relevant experience in place of a degree.\nMinimum Years of Experience:\nMinimum 5 years of professional experience in software engineering, machine learning engineering, or data engineering.\nAt least 2 years designing, building, and deploying LLM or machine learning applications that real operational or business users rely on.\nPreferred Qualifications:\nPrior roles as an AI Engineer, Machine Learning Engineer, Software Engineer, Data Engineer, or Solution Architect with direct build responsibility.\nExperience supporting Intelligence Community, DoD, or Federal customers.\nExperience 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.\nExperience building agentic workflows that call tools, APIs, or other systems, including Model Context Protocol (MCP) or similar integration standards.\nExperience with MLOps or LLMOps practices, including model versioning, CI/CD for models, monitoring, and drift detection.\nExperience preparing or contributing to ATO packages for systems that include AI or machine learning components.\nExperience with enterprise AI platforms such as Microsoft 365 Copilot, Azure AI Services, or Amazon Bedrock in Government cloud regions.\nExperience coaching teams on effective AI use and building business cases that quantify the productivity gains from AI adoption.\nCompensation Details:\n$195,000 - $250,000The compensation range or hourly rate listed for this position is provided as a good-faith estimate of what the company intends to offer for this role at the time this posting was issued. Actual compensation may vary based on factors such as job responsibilities, education, experience, skills, internal equity, market data, applicable collective bargaining agreements, and relevant laws.\nBenefits Overview:\nOur health and welfare benefits are designed to support you and your priorities. Offerings include:\nHealth, dental, and vision insurance\n\nPaid time off and holidays\n\nRetirement benefits (including 401(k) matching)\n\nEducational reimbursement\n\nParental leave\n\nEmployee stock purchase plan\n\nTax-saving options\n\nDisability and life insurance\n\nPet insurance\n\nNote: Benefits may vary based on employment type, location, and applicable agreements. Positions governed by a Collective Bargaining Agreement (CBA), the McNamara-O'Hara Service Contract Act (SCA), or other employment contracts may include different provisions/benefits.\nOriginal Posting:\n10/12/2026 - Until FilledAmentum anticipates this job requisition will remain open for at least three days, with a closing date no earlier than three days after the original posting. This timeline may change based on business needs.\nAmentum is proud to be an Equal Opportunity Employer. Our hiring practices provide equal opportunity for employment without regard to race, sex, sexual orientation, pregnancy (including pregnancy, childbirth, breastfeeding, or medical conditions related to pregnancy, childbirth, or breastfeeding), age, ancestry, United States military or veteran status, color, religion, creed, marital or domestic partner status, medical condition, genetic information, national origin, citizenship status, low-income status, or mental or physical disability so long as the essential functions of the job can be performed with or without reasonable accommodation, or any other protected category under federal, state, or local law. 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