AI Project Lead

AI Project Lead

KCDA's AI Project Lead will help scope, plan and deliver AI-enabled solutions to assist prosecutor workflows. The AI Project Lead will report to the Director of Application Development as part of the Application Development Team, and work collaboratively with Executive stakeholders and end-users to architect scalable AI/ML solutions. The AI Project Lead will act as a trusted advisor in responsibly building tools to serve the Office's core mission of seeking justice.

In this role, the AI project lead will lead a team of developers to participate in early-stage ideation sessions, survey prosecutor defined use-cases, explore multiple solutions, build technology stacks, test and train products for viable outcomes, and deploy enterprise-wide solutions. A successful candidate must have technical skills required for AI application development, project management experience, and a demonstrated ability for strategic planning.

Key responsibilities include:

• Analyze, design, implement, maintain, and support AI applications and monitoring solutions for enterprise infrastructure systems and platforms.
• Knowledge of infrastructure for AI/ML related tasks (Cloud and on-prem) is required.
• Technical competency in Python is required.
• Proficiency with AI application architecture scoping and design, and proficiency with LLM fine tuning using frameworks such as Hugging Face, LoRA, or PEFT.
• Account for and mitigate risks during design and implementation regarding data security, data integrity, and AI veracity; implement AI audit logging, explainability mechanisms, and human-in-the-loop workflows.
• Proficiency with implementing LLM guardrails including hallucination mitigation, PII detection and output validation.
• Review and draft training and internal governance, ensuring compliance with agency standards, propose updates to agency standards in line with industry advancements.
• Train, upskill, supervise, and mentor project teams.
• Communicate with end-users, executive stakeholders and IT supervision regarding use cases, project plans, and recommendations.
• Test local solutions; manage on-premises GPU infrastructure for AI inference and training.
• Test cloud compute. Experience with Azure, AWS, and/or GCP is required.
• Design and deploy retrieval-augmented generation (RAG) pipelines, test hybrid environments.
• Oversee testing and reporting of quality assurance and quality control of AI use cases.
• Oversee scale-up, training, and deployment.
• Oversee Event Management, Patch Management, Release Management, Change Management development and processes.
• Monitor industry developments, liaise with vendors regarding new products and technologies.