Job description

GET TO KNOW US: LeoTech is leading the effort to assist public safety efforts around the nation. We bring industry expertise and innovative technology to address real-world problems public safety agencies face every day. Our search and analytics platform, Verus, has been used to assist in homeland security, provide assistance for mental health, and increase safety and awareness of public safety. Everything we do and build is aligned with our mission and the goals of our public safety partners. We work hand-in-hand to build technology solutions that drive the future of public safety. ROLE 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. Requirements 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 • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. • ·5+ years of hands-on experience in applied AI, NLP, or ML engineering (with at least 2 years working directly with LLMs, RAG, semantic search and Agentic AI). • Deep familiarity with LLMs (e.g. OpenAI, Claude, Gemini), prompt engineering, and responsible deployment in production settings. • Experience designing, building, and optimizing RAG pipelines, semantic search, vector databases (e.g. ElasticSearch, Pinecone), and Agentic or multi-agent AI workflows in in large scale production setup. Exposure to MCP and A2A protocol is a plus. • Exposure to GraphRAG or graph-based knowledge retrieval techniques is a strong plus. • Strong proficiency with modern ML frameworks and libraries (e.g. LangChain, LlamaIndex, PyTorch, HuggingFace Transformers). • Ability to design APIs and scalable backend services, with hands-on experience in Python. • Experience building, deploying, and monitoring AI/ML workloads in cloud environments (AWS, Azure) using services like AWS SageMaker, AWS Bedrock, AzureAI, etc. Experience with tools to load balance different LLMs providers is a plus. • Familiarity with MLOps practices, CI/CD for AI, model monitoring, data versioning, and continuous integration. • Demonstrated ability to work with large, complex datasets, perform data cleaning, feature engineering, and develop scalable data pipelines. • Excellent problem-solving, collaboration, and communication skills; able to work effectively across remote and distributed teams. • Proven record of shipping robust, high-impact AI solutions, ideally in fast-paced or regulated environments. WHAT'S IN IT FOR YOU • Continuous learning environment. • Opportunity to communicate and work with people of all technical levels in a team environment. • Grow as you are given feedback and incorporate it into your work. • Be part of a self-managing team that enjoys support and direction when required. • 3 weeks of paid vacation. • Competitive Salary. • Generous medical, dental, and vision plans. • Sick and paid holidays are offered. • Work with talented and collaborative co-workers • Work in a dynamic and fast-paced environment with challenging problems to solve. • Enjoy great team camaraderie, whether at our client locations or working remotely. • Modern technologies and tools. LeoTech recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law.

Requirements

Must-haves

LeoTech is leading the effort to assist public safety efforts around the nation. We bring industry expertise and innovative technology to address real-world problems public safety agencies face every day.
Our search and analytics platform, Verus, has been used to assist in homeland security, provide assistance for mental health, and increase safety and awareness of public safety.
Everything we do and build is aligned with our mission and the goals of our public safety partners. We work hand-in-hand to build technology solutions that drive the future of public safety.
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.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
5+ years of hands-on experience in applied AI, NLP, or ML engineering (with at least 2 years working directly with LLMs, RAG, semantic search and Agentic AI).
Deep familiarity with LLMs (e.g. OpenAI, Claude, Gemini), prompt engineering, and responsible deployment in production settings.
Exposure to GraphRAG or graph-based knowledge retrieval techniques is a strong plus.
Strong proficiency with modern ML frameworks and libraries (e.g. LangChain, LlamaIndex, PyTorch, HuggingFace Transformers).
Ability to design APIs and scalable backend services, with hands-on experience in Python.
Familiarity with MLOps practices, CI/CD for AI, model monitoring, data versioning, and continuous integration.
Demonstrated ability to work with large, complex datasets, perform data cleaning, feature engineering, and develop scalable data pipelines.
Excellent problem-solving, collaboration, and communication skills; able to work effectively across remote and distributed teams.
Proven record of shipping robust, high-impact AI solutions, ideally in fast-paced or regulated environments.

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aillmmlpythonawsazureci/cd

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Effort: medium

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