AI Technical Lead

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.

5 days a week, 8 hours a day

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.

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.

• 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 (Lang Chain, Llama Index, Semantic Kernel, or similar), plus containerization with Docker and Kubernetes.
• Abili…