Role Overview
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.
What you can Expect
- 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.
What we Value
- Problem Solving/Analysis.
- Technical Capacity.
- Communication Proficiency.
- Time Management.
- Ability to work as part of a team.