AI Technical Lead

Amentum•Hanover, MD

1d•$200,000 - $300,000•Hybrid

About The Position

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.

Requirements

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.
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.
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.
Active TS/SCI with POLY
Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.
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.

Nice To Haves

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.

Responsibilities

Defining the artificial intelligence strategy
Overseeing technical execution
Guiding cross-functional teams to build scalable AI models and applications
Managing the AI lifecycle
Selecting appropriate frameworks
Ensuring ethical data governance and compliance
Provide mentorship to AI team
Communicating technical milestones to stakeholders to drive innovation and business growth
Leading a team of AI, software, or data engineers through full delivery cycles, including hiring, mentoring, performance management, and career development
Setting technical direction across several AI efforts at once and deciding where the team builds, buys, or partners
Owning AI governance for a program or organization, including model approval, usage policy, and alignment with customer security requirements
Building and running an AI Center of Excellence or community of practice that spread AI use across multiple programs
Supporting business development and capture, including solution architecture, technical volumes, BOEs, and orals
Representing the company with senior Government stakeholders and industry partners and turning mission needs into a funded AI roadmap
Building business cases and IRAD investment priorities that show measurable return