Principal AI Engineer

Chainguard is a company focused on providing secure software development and deployment solutions. The Staff Engineer, AI Solutions will lead the design and implementation of AI-powered systems to enhance the company's go-to-market operations, collaborating with various stakeholders to translate business needs into scalable technical solutions.

Responsibilities:

• Experiment Constantly! Design, build, and operate production-grade AI solutions that support GTM use cases, including account intelligence, deal prioritization, forecasting, personalization, enablement, and customer insights
• Evaluate new AI technologies, frameworks, and vendors, contributing to build vs. buy decisions and technical direction
• Lead the technical architecture and implementation of GenAI and ML systems, including LLM-powered applications, retrieval-augmented generation (RAG), recommendation systems, and predictive models
• Partner closely with the Senior Director of AI Solutions and GTM stakeholders to translate business requirements into scalable technical designs
• Own the end-to-end lifecycle of AI solutions, from data ingestion and feature development to model deployment, monitoring, and iteration
• Integrate AI capabilities into existing GTM platforms and workflows (e.g., Salesforce, internal tools, analytics platforms)
• Establish best practices for AI engineering, including model evaluation, observability, performance optimization, and reliability
• Collaborate with Data, GTM Systems, Security, and Legal teams to ensure solutions meet data quality, privacy, security, and compliance standards
• Drive adoption by ensuring AI solutions are usable, trusted, and embedded into how GTM teams work day to day

Requirements:

• 8+ years of experience in software engineering, data engineering, or ML engineering, with hands-on experience delivering AI-powered systems to production
• Strong practical experience with Generative AI and LLM-based systems, including prompt engineering, RAG architectures, and evaluation techniques
• Experience building and deploying machine learning models or intelligent systems that support decision-making or automation
• Solid understanding of data pipelines, feature engineering, APIs, and system integration, even if you're not a full-time data platform engineer
• Proficiency in modern programming languages commonly used in AI systems (e.g., Python, SQL, and/or backend languages such as Go or Java)
• Experience integrating AI solutions into business applications or GTM systems, such as CRM, analytics, or internal tooling
• Strong architectural judgment, with the ability to make tradeoffs between speed, quality, scalability, and maintainability