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Checkr is a data platform powering safe and fair decisions, trusted by 140,000+ companies including Uber, Airbnb, DoorDash, and Anthropic. The company is on the Forbes Cloud 100 2025 list and recognized as a Y Combinator 2024 Breakthrough Company.
As Staff AI Solutions Engineer, you will be the primary driver of AI enablement across Checkr's organization. This is a highly visible, hands-on role blending deep technical expertise with a passion for teaching and empowering teams to unlock AI's potential.
Key responsibilities include:
- Embed with internal teams across departments to understand workflows, gather requirements, and identify high-impact AI use cases
- Lead recurring AI office hours for drop-in support and design company-wide AI hackathons
- Evaluate, configure, and recommend AI tools and platforms; stay current with the rapidly evolving AI tooling landscape
- Design, develop, and deploy custom AI solutions leveraging LLMs, RAG architectures, agentic workflows, and automation frameworks
- Create reusable playbooks, prompt libraries, workflow templates, and reference architectures for self-serve AI adoption
- Architect standardized, templatizable AI workflows that scale across multiple teams and use cases
- Lead hands-on training sessions, workshops, and demos to build AI fluency organization-wide
- Partner with Engineers, Business Systems, Security, and Data Engineering to integrate AI technologies securely and at scale
- Contribute to organizational AI strategy and roadmapping aligned with business priorities
- Present findings, demo solutions, and advocate for AI adoption with stakeholders from individual contributors to executives
Required qualifications:
- Bachelor's degree in Computer Science or equivalent experience (graduate degree a plus)
- 5+ years in engineering, solutions engineering, or technical enablement roles with proven track record of deploying productivity-enhancing solutions
- 3+ years hands-on developing AI-centric products and solutions including machine learning, generative AI, LLMs, RAG architectures, agentic frameworks, and prompt engineering
- Deep Python proficiency and experience with AI frameworks and LLM model integration; familiarity with AWS, GCP, or Azure
- Demonstrated ability to lead enablement programs including office hours, hackathons, training, and scalable documentation
- Proven experience gathering requirements from non-technical stakeholders and translating into well-scoped technical solutions
- Exceptional ability to tackle open-ended, ambiguous problems in unstructured environments
- Strong communication and presentation skills making complex AI concepts accessible to diverse audiences
