Machine Learning Engineer

Responsibilities

• Design and fine-tune LLM pipelines to interpret complex regulatory texts and extract structured rules
• Implement RAG (Retrieval-Augmented Generation) architectures for semantic querying of technical documentation
• Develop time-series forecasting models to predict material demand and spend categories
• Build classification and anomaly detection models to assess supplier risk profiles
• Containerize and deploy models using Docker and Kubernetes into secure, on-premise environments
• Build automated training and inference pipelines using tools like Kubeflow or MLflow
• Optimize model inference latency and resource usage through quantization and distillation
• Implement monitoring systems to track model drift and establish continuous improvement feedback loops

Requirements

• 5+ years of experience in Machine Learning Engineering with a track record of production deployment
• Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy)
• Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain)
• Proficiency with MLOps tools including Docker, Kubernetes, and MLflow
• Ability to design data preprocessing pipelines for both structured and unstructured data
• Strong grasp of algorithmic principles for implementing custom logic

Preferred Qualifications

• Experience working in agile environments (Sprints) within highly regulated or defense-grade sectors
• Ability to quickly adapt ML techniques to specialized domains like supply chain or defense engineering
• Experience with multi-objective optimization algorithms

About the Company

AI71 is building the intelligence that drives critical national infrastructure. We develop advanced AI models that influence the design of defense systems and the resilience of global supply chains, operating at the intersection of generative AI and high-stakes engineering.