AI Specialist

Role Overview

• Own the design, development, training, and ongoing refinement of KnoxAI models and systems, ensuring they meet evolving business, security, and compliance requirements
• Develop and maintain high-quality training pipelines, including dataset selection, labeling strategies, evaluation frameworks, and continuous feedback loops
• Improve model performance, reliability, explainability, and robustness through experimentation, tuning, and systematic evaluation
• Partner closely with Product and Engineering to translate real-world Knox use cases into production AI capabilities, not prototypes
• Collaborate with Security and Compliance teams to ensure AI systems align with federal requirements, audit-ability expectations, and risk management standards
• Implement monitoring and retraining strategies to detect drift, performance degradation, or emerging risks over time
• Contribute to architectural decisions around AI infrastructure, deployment, and MLOps in secure, regulated environments
• Document model behavior, assumptions, limitations, and decision logic to support internal understanding and external scrutiny
• Stay current on advances in AI, machine learning, and data science, selectively applying new techniques where they materially improve KnoxAI
• Remain hands-on while helping establish repeatable, scalable AI development practices as Knox grows

Requirements

• Strong foundation in data science, machine learning, and applied AI, with demonstrated experience building and operating models used in real production systems
• Advanced proficiency in Python and modern ML / AI frameworks, with the ability to move comfortably between experimentation and production code
• Working proficiency in Node.js and/or Bun.js, with experience integrating AI and ML systems into production application backends and services
• Hands-on experience with data-centric AI practices, including dataset design, curation, labeling strategies, versioning, and managing data quality over time
• Proven ability to train, fine-tune, and evaluate models using rigorous validation approaches, with a clear understanding of tradeoffs between accuracy, precision, recall, and operational risk
• Experience designing and applying custom evaluation metrics aligned to real-world outcomes, including managing false positives and false negatives in high-stakes systems
• Experience operationalizing AI models in production environments, including deployment, monitoring, performance tracking, drift detection, retraining workflows, and rollback strategies
• Familiarity with MLOps practices and tooling, including model versioning, CI/CD for ML, and lifecycle management
• Ability to design explainable and auditable AI systems, with experience documenting model behavior, assumptions, limitations, and decision logic for internal and external stakeholders
• Strong systems-level thinking, with the ability to reason across data, models, infrastructure, security, and users to build durable, maintainable AI solutions
• Proven ability to operate independently, take ownership, and drive complex work forward in fast-moving, high-accountability environments
• Strong communication skills, including the ability to clearly explain complex AI concepts and tradeoffs to non-technical audiences.

Tech Stack

• JavaScript
• Node.js
• Python

Benefits

• Medical
• Dental
• Vision
• Life & Disability
• Unlimited PEO
• Employee funded 401k plan