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Salary: USD 225,000 - 320,000 / annual
Peregrine Technologies is an AI-enabled intelligence platform serving public safety organizations, government agencies, and enterprises across 30+ states and two countries. As Applied AI Engineering Lead, you will own the design, deployment, and operationalization of AI-powered features that turn complex, siloed data into trusted operational intelligence for high-consequence decision-making environments.
You will lead the development of customer-facing AI capabilities that help detect risk, surface insights, and enable faster, better decisions. This role bridges product strategy and technical execution, requiring you to translate real operational problems into deployable AI solutions while maintaining high reliability, transparency, and performance.
Key responsibilities include: designing and deploying AI features across the platform in partnership with Product and Deployment teams; owning fine-tuning strategies and reinforcement learning approaches to improve decision quality; making principled technical tradeoffs between classical ML, deep learning, and LLM-based solutions based on accuracy, latency, and risk; shaping AI infrastructure including training/inference pipelines, model serving, versioning, and integration with real-time and batch data systems; defining robust evaluation frameworks and quality standards that reflect real-world decision impact; establishing monitoring, drift detection, and failure analysis for deployed models; and building and leading a high-performing Applied AI/ML team while setting technical direction and partnering with executive leadership on AI strategy and responsible deployment.
Required qualifications include demonstrated experience shipping AI-powered features into production (ideally in enterprise or mission-critical environments), hands-on experience with model fine-tuning for LLMs or deep learning, practical exposure to reinforcement learning, experience designing and operating AI/ML infrastructure, direct ownership of model evaluation and quality measurement, and strong software engineering fundamentals as a leader. Strong pluses include experience with large-scale or real-time data platforms, background in domains where trust and explainability matter (public sector, security, healthcare, finance), and prior software engineering team leadership.
