Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field
3–5+ years of hands-on ML/AI engineering experience, including at least 2 years working directly on LLM evaluation, QA, or safety
Strong familiarity with evaluation techniques for generative AI (human-in-the-loop evaluation, automated metrics, adversarial testing, red-teaming) and bias detection/fairness
Proficiency with Python and modern AI/ML/LLM libraries (e.g., LangChain, HuggingFace, PyTorch, LlamaIndex) and experience with observability/guardrails tools (Langfuse, Langsmith) and DevOps/MLOps pipelines (Kubernetes, Terraform, ArgoCD, GitHub Actions)
Requirements:
Build and maintain evaluation frameworks for LLMs and generative AI systems tailored to public safety use cases; design guardrails and alignment strategies to minimize bias, toxicity, and hallucinations in production workflows
Define online and offline evaluation metrics (e.g., model drifts, data drifts, factual accuracy, consistency, safety, interpretability) and implement continuous evaluation pipelines integrated into CI/CD and production monitoring systems
Stress test models against edge cases, adversarial prompts, and sensitive data scenarios; research and integrate third-party evaluation frameworks adapted to a regulated, high-stakes environment
Ensure explainability, transparency, and auditability of AI outputs; provide technical leadership in responsible AI practices and contribute to DevOps/MLOps workflows for deployment, monitoring, and scaling of evaluation and guardrail systems
Job description
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.
Role
This is a remote, WFH role.
As an AI/LLM Evaluation & Alignment Engineer on our Data Science team, you will play a critical role in ensuring that our Large Language Model (LLM) and Agentic AI solutions are accurate, safe, and aligned with the unique requirements of public safety and law enforcement workflows. You will design and implement evaluation frameworks, guardrails, and bias-mitigation strategies that give our customers confidence in the reliability and ethical use of our AI systems. This is an individual contributor (IC) role that combines hands-on technical engineering with a focus on responsible AI deployment. You will work closely with AI engineers, product managers, and DevOps teams to establish standards for evaluation, design test harnesses for generative models, and operationalize quality assurance processes across our AI stack.
Core Responsibilities
Build and maintain evaluation frameworks for LLMs and generative AI systems tailored to public safety and intelligence use cases.
Design guardrails and alignment strategies to minimize bias, toxicity, hallucinations, and other ethical risks in production workflows.
Partner with AI engineers and data scientists to define online and offline evaluation metrics (e.g., model drifts, data drifts, factual accuracy, consistency, safety, interpretability).
Implement continuous evaluation pipelines for AI models, integrated into CI/CD and production monitoring systems.
Collaborate with stakeholders to stress test models against edge cases, adversarial prompts, and sensitive data scenarios.
Research and integrate third-party evaluation frameworks and solutions; adapt them to our regulated, high-stakes environment.
Work with product and customer-facing teams to ensure explainability, transparency, and auditability of AI outputs.
Provide technical leadership in responsible AI practices, influencing standards across the organization.
Contribute to DevOps/MLOps workflows for deployment, monitoring, and scaling of AI evaluation and guardrail systems (experience with Kubernetes is a plus).
Document best practices and findings, and share knowledge across teams to foster a culture of responsible AI innovation.
What We Value
Bachelor's or Master's in Computer Science, Artificial Intelligence, Data Science, or related field.
3–5+ years of hands-on experience in ML/AI engineering, with at least 2 years working directly on LLM evaluation, QA, or safety.
Strong familiarity with evaluation techniques for generative AI: human-in-the-loop evaluation, automated metrics, adversarial testing, red-teaming.
Experience with bias detection, fairness approaches, and responsible AI design.
Knowledge of LLM observability, monitoring, and guardrail frameworks e.g Langfuse, Langsmith
Proficiency with Python and modern AI/ML/LLM/Agentic AI libraries (LangGraph, Strands Agents, Pydantic AI, LangChain, HuggingFace, PyTorch, LlamaIndex).
Experience integrating evaluations into DevOps/MLOps pipelines, preferably with Kubernetes, Terraform, ArgoCD, or GitHub Actions.
Understanding of cloud AI platforms (AWS, Azure) and deployment best practices.
Strong problem-solving skills, with the ability to design practical evaluation systems for real-world, high-stakes scenarios.
Excellent communication skills to translate technical risks and evaluation results into insights for both technical and non-technical stakeholders.
