Applied AI Engineering Lead — Production‑Ready AI Leader

Peregrine; San Francisco (CA); On-site; Full time

Job description

Requirements

• Demonstrated experience shipping AI-powered features into production, ideally in enterprise or mission critical environments
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• Hands-on experience with model fine tuning (LLMs or deep learning models)
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• Practical exposure to reinforcement learning
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• Experience in designing and operating AI/ML infrastructure
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• Direct ownership of model evaluation, experimentation, and quality measurement
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• Strong software engineering fundamentals as a lead
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• (Desirable) Experience working with large scale and/or real-time data platforms
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• (Desirable) Background in domains where trust, explainability, and reliability matter (e.g., public sector, security, healthcare, finance)
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• (Desirable) Led software engineering teams before

What the job involves

• Peregrine builds an end-to-end intelligence platform
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• As the Lead of Applied AI, you will own how artificial intelligence is applied, evaluated, and operationalized across our product, turning complex data into trusted, explainable, and high-impact capabilities used in the field
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• This role is about production AI in high-consequence environments. You’ll lead to develop AI-powered features that help customers detect risk, surface insights, and make faster, better decisions while maintaining high reliability, transparency, and performance
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• Lead the design and deployment of customer facing AI features across Peregrine’s platform
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• Translate real operational problems into deployable AI-powered solutions
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• Partner deeply with Product, Deployment Strategists, and other Engineering teams to integrate AI into core workflows
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• Own and guide fine tuning strategies and reinforcement learning to improve decision quality
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• Make principled tradeoffs between classical ML, deep learning, and LLM-based approaches based on accuracy, latency, and risk
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• Shape Peregrine’s AI infrastructure, including:
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• Training and inference pipelines
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• Model serving, versioning, and rollback
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• Integration with real-time and batch data systems
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• Ensure models are observable, scalable, secure, and cost-effective in production
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• Meet enterprise and government requirements
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• Define and operate robust evaluation frameworks
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• Establish quality standards that reflect real-world decision impact, not just model accuracy
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• Own monitoring, drift detection, and failure analysis for deployed models
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• Build and lead a high-performing Applied AI/ML team
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• Set technical direction for applied AI across Peregrine
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• Partner with executive leadership on AI strategy, roadmap, and responsible deployment