Applied AI Engineer
Builds and deploys language model-powered systems for cyber national security applications, including fine-tuning LLMs, RAG systems, and production inference. Requires 4+ years ML experience, Python/PyTorch proficiency, and LLM post-training expertise.
About the job
Responsibilities
- Create, clean, and maintain high-quality training and evaluation datasets for specialized AI use cases.
- Fine-tune language models (small specialized through medium foundation models) for mission needs.
- Implement post-training and alignment approaches to improve task performance and reliability.
- Build retrieval-augmented generation (RAG) systems that ground model outputs in external knowledge.
- Develop and optimize model serving infrastructure for production deployments.
- Design evaluation frameworks and test harnesses to measure quality, latency, and regressions.
- Integrate AI capabilities into applications and workflows using modern orchestration frameworks.
- Collaborate with cross-functional partners to identify high-leverage use cases and deliver solutions.
- Produce clear technical documentation for models, datasets, and operational processes.
Requirements
- 4+ years of professional software development experience building and supporting ML/AI-enabled applications.
- Strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX.
- Hands-on experience with LLM post-training methods (e.g., continued pre-training, SFT, RLHF, DPO, PPO, GRPO).
- Experience curating, cleaning, and preprocessing datasets for training and evaluation.
- Working knowledge of relational, graph, and vector database concepts.
- Experience designing or using evaluation metrics and testing procedures for LLMs and agents.
- Experience integrating LLM/agent systems using frameworks like Pydantic-AI, LangChain/LangGraph, or CrewAI.
- Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent practical experience).
Nice To Haves
- Deployed models to production and supported them through real-world usage and incidents.
- Experience with distributed training systems and performance debugging at scale.
- Implemented quantization or other optimization techniques to improve inference efficiency.
- Strong prompt engineering and model alignment instincts for reliability and control.
- Experience building MLOps/LLMOps/AgentOps practices (versioning, rollout, monitoring).
Skills
Python, PyTorch, TensorFlow, JAX, LangChain, LangGraph, Crewai, Pydantic-Ai, RAG, vLLM, Kubernetes, Docker, Pgvector, Chromadb, Pinecone
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