Description:

LeoTech is seeking an AI/NLP Engineer to join their Data Science team, focusing on public safety solutions.
The role involves leveraging Large Language Models (LLMs) and advanced AI techniques to create transformative solutions for intelligence workflows.
Responsibilities include designing, building, and optimizing AI-powered solutions, implementing advancements in language modeling, and collaborating with various teams to deliver impactful features.
The position emphasizes technical depth, experimentation, and hands-on engineering, participating in all phases of the AI solution lifecycle.
Core responsibilities include integrating external data sources, evaluating best practices for AI deployment, and authoring clean, well-documented code.

Requirements:

A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field is required.
Candidates must have 5+ years of hands-on experience in applied AI, NLP, or ML engineering, with at least 2 years working directly with LLMs, RAG, or semantic search.
Deep familiarity with LLMs, prompt engineering, and responsible deployment in production settings is essential.
Experience in designing and optimizing RAG pipelines, semantic search, and vector databases is required.
Proficiency with modern ML frameworks and libraries, as well as experience in building and deploying AI/ML workloads in cloud environments, is necessary.
Familiarity with MLOps practices and the ability to work with large datasets is expected.
Strong problem-solving, collaboration, and communication skills are essential for effective teamwork.

The position offers a work-from-home opportunity and promotes great team camaraderie.
Employees will enjoy a continuous learning environment with modern technologies and tools.
The role includes 3 weeks of paid vacation from the start, along with a competitive salary.
Generous medical, dental, and vision plans are provided, along with sick leave and paid holidays.
Employees will have the opportunity to grow through feedback and be part of a self-managing team that receives support when needed.