{"id":3094997,"url":"https://alion.io/job/amentum-ai-technical-lead","title":"AI Technical Lead","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":{"grade":"B","score":80,"open_postings":22,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","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":200000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":10,"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":"GraphRAG","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Multimodal AI","optional":true},{"name":"OWASP","optional":true},{"name":"PEFT","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-10-12T00:00:00Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-24T10:52:36Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Amentum is hiring a AI Technical Lead in Hanover, MD. The leader directs the AI engineering team, owns AI strategy and governance across the portfolio, and represents the company's AI capabilities to Government customers and industry partners. The role builds toward assuming the senior AI lead position for the sector.\nWork Schedule:\n5 days a week, 8 hours a day\nEssential Responsibilities:\nResponsible for defining the artificial intelligence strategy, overseeing technical execution, and guiding cross-functional teams to build scalable AI models and applications. They bridge the gap between business objectives and engineering by managing the AI lifecycle, selecting appropriate frameworks, and ensuring ethical data governance and compliance. Additionally, provide mentorship to AI team while communicating technical milestones to stakeholders to drive innovation and business growth.\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.\nTeam Leadership\nExperience leading a team of AI, software, or data engineers through full delivery cycles, including hiring, mentoring, performance management, and career development.\nExperience setting technical direction across several AI efforts at once and deciding where the team builds, buys, or partners.\nExperience owning AI governance for a program or organization, including model approval, usage policy, and alignment with customer security requirements.\nExperience building and running an AI Center of Excellence or community of practice that spread AI use across multiple programs.\nExperience supporting business development and capture, including solution architecture, technical volumes, BOEs, and orals.\nExperience representing the company with senior Government stakeholders and industry partners and turning mission needs into a funded AI roadmap.\nExperience building business cases and IRAD investment priorities that show measurable return.\nSecurity Clearance Required:\nActive TS/SCI with POLY\nMinimum Education:\nBachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.\nWe will consider an equivalent combination of education and relevant experience in place of a degree.\nMinimum Years of Experience:\nMinimum 10 years of professional experience in software engineering, machine learning engineering, data engineering, or Information Technology.\nAt least 3 years leading technical teams.\nAt least 2 years leading AI delivery for operational or business users.\nPreferred Qualifications:\nAbove and Beyond AI Skills\nDesigning agentic and multi-agent systems with human approval checkpoints, including tool integration through MCP or similar standards.\nAI security and adversarial machine learning, including red teaming, data poisoning, model extraction, and prompt injection, using the OWASP LLM Top 10 and MITRE ATLAS.\nRunning small models at the tactical edge through distillation and quantization, for environments with limited or intermittent connectivity.\nMultimodal AI across imagery, full motion video, speech, and geospatial data.\nGraphRAG and knowledge graphs that pair structured relationships with retrieval for analytic work.\nSynthetic data generation for training and testing when real data is classified, scarce, or sensitive.\nAI data flows across classification levels through cross domain solutions.\nPlanning AI cost and capacity, including GPU sizing, inference optimization, and token budgets.\nCurrent Federal and IC AI policy, including OMB, DoD CDAO, and ODNI guidance.\nRolling out AI coding assistants and agents across an engineering team's development process.\nA public record such as publications, conference talks, patents, or open source contributions.\nCompensation Details:\n$200,000 - $300,000The 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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