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