Job 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. Work Schedule: 5 days a week, 8 hours a day Essential Responsibilities: Responsible 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. Work Environment, Physical Demands, and Mental Demands: Hybrid 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. Minimum Knowledge Large language model fundamentals, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection. Retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation. Model adaptation methods, including supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, along with when each approach outperforms prompting or RAG alone. The open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar) and the licensing, provenance, and supply chain considerations for bringing models into Government environments. Security 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. Minimum Skills Demonstrated 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. Demonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and measuring 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 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 so each user retrieves only content that matches their clearance, compartments, and need-to-know. Experience building evaluation and guardrails for LLM output, 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. Minimum Abilities Ability to translate mission requirements into working AI architecture and choose the right approach (prompting, RAG, fine-tuning, or agents) for each problem. Ability to evaluate emerging models and tools quickly and recommend which merit 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. Team Leadership Experience leading a team of AI, software, or data engineers through full delivery cycles, including hiring, mentoring, performance management, and career development. Experience setting technical direction across several AI efforts at once and deciding where the team builds, buys, or partners. Experience owning AI governance for a program or organization, including model approval, usage policy, and alignment with customer security requirements. Experience building and running an AI Center of Excellence or community of practice that spread AI use across multiple programs. Experience supporting business development and capture, including solution architecture, technical volumes, BOEs, and orals. Experience representing the company with senior Government stakeholders and industry partners and turning mission needs into a funded AI roadmap. Experience building business cases and IRAD investment priorities that show measurable return. Security Clearance Required: Active TS/SCI with POLY Minimum Education: Bachelor s degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field. We will consider an equivalent combination of education and relevant experience in place of a degree. Minimum Years of Experience: Minimum 10 years of professional experience in software engineering, machine learning engineering, data engineering, or Information Technology. At least 3 years leading technical teams. At least 2 years leading AI delivery for operational or business users. Preferred Qualifications: Above and Beyond AI Skills Designing agentic and multi-agent systems with human approval checkpoints, including tool integration through MCP or similar standards. AI security and adversarial machine learning, including red teaming, data poisoning, model extraction, and prompt injection, using the OWASP LLM Top 10 and MITRE ATLAS. Running small models at the tactical edge through distillation and quantization, for environments with limited or intermittent connectivity. Multimodal AI across imagery, full motion video, speech, and geospatial data. GraphRAG and knowledge graphs that pair structured relationships with retrieval for analytic work. Synthetic data generation for training and testing when real data is classified, scarce, or sensitive. AI data flows across classification levels through cross domain solutions. Planning AI cost and capacity, including GPU sizing, inference optimization, and token budgets. Current Federal and IC AI policy, including OMB, DoD CDAO, and ODNI guidance. Rolling out AI coding assistants and agents across an engineering team s development process. A public record such as publications, conference talks, patents, or open source contributions. #javelin Compensation Details: $200,000 - $300,000 The 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. Benefits Overview: Our health and welfare benefits are designed to support you and your priorities. Offerings include: 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 Note: 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. Original Posting: 10/12/2026 - Until Filled Amentum 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. Amentum 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. Learn more about your rights under Federal laws and supplemental language at Labor Laws Posters .

Requirements

Must-haves

5 days a week, 8 hours a day
Work Environment, Physical Demands, and Mental Demands:
Minimum Knowledge
Large language model fundamentals, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection.
Retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation.
Model adaptation methods, including supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, along with when each approach outperforms prompting or RAG alone.
The open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar) and the licensing, provenance, and supply chain considerations for bringing models into Government environments.
Security 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.
Demonstrated 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.
Demonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and measuring 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 capabilities in disconnected or classified environments, including moving models, containers, and dependencies across the air gap and supporting the system through ATO.

Nice-to-haves

Above and Beyond AI Skills
Designing agentic and multi-agent systems with human approval checkpoints, including tool integration through MCP or similar standards.
AI security and adversarial machine learning, including red teaming, data poisoning, model extraction, and prompt injection, using the OWASP LLM Top 10 and MITRE ATLAS.
Running small models at the tactical edge through distillation and quantization, for environments with limited or intermittent connectivity.
Multimodal AI across imagery, full motion video, speech, and geospatial data.
GraphRAG and knowledge graphs that pair structured relationships with retrieval for analytic work.
Synthetic data generation for training and testing when real data is classified, scarce, or sensitive.
AI data flows across classification levels through cross domain solutions.
Planning AI cost and capacity, including GPU sizing, inference optimization, and token budgets.
Current Federal and IC AI policy, including OMB, DoD CDAO, and ODNI guidance.
Rolling out AI coding assistants and agents across an engineering team s development process.
A public record such as publications, conference talks, patents, or open source contributions.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythondockerkubernetesmachine learning

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave