AI/ML Engineer

Role Overview

As an AI/NLP Engineer on the Data Science team, you will lead the design and deployment of Large Language Model (LLM)-powered solutions for public safety and intelligence applications. This is an individual contributor role emphasizing technical depth, experimentation, and hands-on engineering across the full AI solution lifecycle.

Key Responsibilities

• Design, build, and optimize AI-powered solutions using LLMs, RAG pipelines, semantic search, GraphRAG, and Agentic AI architectures
• Implement and experiment with advanced language modeling techniques including prompt engineering, model fine-tuning, evaluation, and monitoring
• Collaborate with product, backend, and data engineering teams to define requirements and deliver high-impact features aligned with business objectives
• Build and maintain robust data ingestion and retrieval pipelines for real-time and batch AI applications
• Integrate external data sources (knowledge graphs, databases, APIs) to enhance context-awareness of LLM workflows
• Evaluate and implement best practices for prompt design, model alignment, safety, and guardrails for responsible AI deployment
• Stay current with emerging AI research and contribute to internal knowledge-sharing and proof-of-concept projects
• Author clean, well-documented code; participate in peer reviews and design discussions
• Identify system bottlenecks and propose solutions to improve scalability, efficiency, and reliability

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field
• 5+ years of hands-on applied AI, NLP, or ML engineering experience
• At least 2 years working directly with LLMs, RAG, semantic search, and Agentic AI
• Deep familiarity with LLMs (OpenAI, Claude, Gemini) and prompt engineering in production settings
• Experience designing, building, and optimizing RAG pipelines and vector databases (ElasticSearch, Pinecone)
• Strong proficiency with ML frameworks: LangChain, LlamaIndex, PyTorch, HuggingFace Transformers
• Hands-on Python experience and ability to design scalable backend services
• Experience building, deploying, and monitoring AI/ML workloads in cloud environments (AWS, Azure)
• Familiarity with MLOps practices, CI/CD for AI, model monitoring, and data versioning
• Experience with large, complex datasets; ability to perform data cleaning and feature engineering
• Excellent problem-solving, collaboration, and communication skills
• Proven track record of shipping robust, high-impact AI solutions

Nice-to-Haves

• Exposure to MCP and A2A protocols
• Experience with GraphRAG or graph-based knowledge retrieval
• Experience with LLM load balancing tools
• Background in fast-paced or regulated environments