1 month ago
Hanover, MD, USASenior
Base Salary
$195k - $250k/yr
Responsibilities
- Design, build, and deploy AI systems that integrate machine-learning models into production software.
- Develop RAG applications, including document ingestion, chunking, embedding selection, vector search, reranking, and retrieval evaluation.
- Fine-tune and adapt open-weight language models using methods such as LoRA, QLoRA, supervised fine-tuning, and distillation.
- Host and serve models on GPU infrastructure while managing sizing and quantization tradeoffs.
- Deploy and support AI capabilities in disconnected and classified environments, including air-gap transfers and ATO support.
- Build access-controlled retrieval pipelines that enforce clearance, compartment, and need-to-know requirements.
- Develop LLM evaluations, guardrails, red-team testing, hallucination measurement, and prompt-injection defenses.
- Build document pipelines for large unstructured collections with parsing, OCR, metadata tagging, and classification-marking preservation.
- Translate mission requirements into AI architectures and explain technical tradeoffs to technical and nontechnical audiences.
- Lead technical work across engineering, security, mission, customer, and Government leadership teams.
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field, or an equivalent combination of education and relevant experience.
- At least 5 years of professional experience in software engineering, machine-learning engineering, or data engineering.
- At least 2 years designing, building, and deploying LLM or machine-learning applications relied on by real operational or business users.
- Demonstrated experience building and deploying at least one production RAG application, including ingestion, chunking, embeddings, vector stores, and retrieval evaluation.
- Experience fine-tuning or adapting open-weight LLMs and measuring results against a defined benchmark.
- Experience serving models on GPU infrastructure using vLLM, TGI, NVIDIA Triton, or similar technologies.
- Experience deploying AI capabilities in disconnected or classified environments and supporting systems through ATO.
- Experience building LLM evaluations, guardrails, hallucination testing, red teaming, and prompt-injection defenses.
- Proficiency in Python, an LLM orchestration framework, Docker, and Kubernetes.
- Active TS/SCI security clearance with Polygraph.
- Preferred experience with Intelligence Community, DoD, or Federal customers; classified cloud regions; Government-operated GPU clusters; agentic workflows; MCP; MLOps or LLMOps; AI-related ATO packages; Microsoft 365 Copilot; Azure AI Services; Amazon Bedrock; and AI adoption coaching or business-case development.
Benefits
- 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.
- Hybrid or modern office environment in Hanover, Maryland; five days per week and eight hours per day.
- Benefits may vary by employment type, location, collective bargaining agreement, or other employment contract.
Tech Stack
Categories
About Amentum
Amentum provides engineering, operations and maintenance, logistics, and mission support services to U.S. federal agencies and allied governments, including defense and energy programs. Its business centers on large, long-term government contracts spanning defense, space, nuclear cleanup, and infrastructure, with program management and technical services at the core. Headquartered in Chantilly, Virginia, it was formed in 2020 from the spinoff of AECOM’s Management Services division.