Applied AI Engineer

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).