Responsibilities
- 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.
Nice to Have
- Experience working with LLMs, retrieval-augmented generation (RAG), or applied AI systems.
- Experience operating in regulated, security-sensitive, or compliance-driven environments.
- Exposure to cloud infrastructure (AWS, Azure, or GCP) and secure system architectures.
- Background in government, federal contracting, cybersecurity, or compliance-adjacent domains.
- Strong written and verbal communication skills, especially the ability to explain complex AI concepts to non-experts.
Additional Information
- Due to the nature of our work with federal government clients and compliance with applicable regulations, this position requires U.S. citizenship. Candidates must be able to provide documentation verifying U.S. citizenship status as part of the background check process. Any offer of employment is contingent upon the successful completion of all required pre-employment screenings, including a background check, in accordance with applicable laws and government contract requirements.
