At LeoTech, we are passionate about building software that solves real-world problems in the Public Safety sector. Our software has been used to help the fight against continuing criminal enterprises, drug trafficking organizations, identifying financial fraud, disrupting sex and human trafficking rings and focusing on mental health matters to name a few.
As an AI/NLP Engineer on our Data Science team, you will be at the forefront of leveraging Large Language Models (LLMs) and cutting-edge AI techniques to create transformative solutions for public safety and intelligence workflows. You will apply your expertise in LLMs, Retrieval-Augmented Generation (RAG), semantic search, Agentic AI, GraphRAG, and other advanced AI solutions to develop, enhance, and deploy robust features that enable real-time decision-making for our end users. You will work closely with product, engineering, and data science teams to translate real-world problems into scalable, production-grade solutions. This is an individual contributor (IC) role that emphasizes technical depth, experimentation, and hands-on engineering. You will participate in all phases of the AI solution lifecycle, from architecture and design through prototyping, implementation, evaluation, productionization and continuous improvement.

• Design, build, and optimize AI-powered solutions using LLMs, RAG pipelines, semantic search, GraphRAG, and Agentic AI architectures.
• Implement and experiment with the latest advancements in large-scale language modeling, including prompt engineering, model fine-tuning, evaluation, and monitoring.
• Collaborate with product, backend, and data engineering teams to define requirements, break down complex problems, and deliver high-impact features aligned with business objectives.
• Inform robust data ingestion and retrieval pipelines that power real-time and batch AI applications using open-source and proprietary tools.
• Integrate external data sources (e.g., knowledge graphs, internal databases, third-party APIs) to enhance the context-awareness and capabilities of LLM-based workflows.
• Evaluate and implement best practices for prompt design, model alignment, safety, and guardrails for responsible AI deployment.
• Stay on top of emerging AI research and contribute to internal knowledge-sharing, tech talks, and proof-of-concept projects.
• Author clean, well-documented, and testable code; participate in peer code reviews and engineering design discussions.
• Proactively identify bottlenecks and propose solutions to improve system scalability, efficiency, and reliability.