# Staff AI Solutions Engineer

**Company:** [Checkr](https://hotfix.jobs/companies/checkr)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $177k – $208k/yr
**Experience:** 5+ years
**Skills:** Python, LangChain, LangGraph, Llamaindex, OpenAI API, LLMs, RAG, Docker, Kubernetes, AWS, GCP, Azure, Prompt Engineering, Machine Learning, Generative AI
**Posted:** 2026-04-02

> Leads AI enablement across Checkr by gathering requirements, building custom LLM/RAG solutions, leading hackathons and training, and creating scalable workflows. Requires 5+ years engineering experience, 4+ years AI development, Python proficiency, and strong communication skills.

## Job Description

## What you’ll do:

- **Requirements Gathering & Enablement**: Embed with internal teams across departments to deeply understand their workflows, gather requirements, identify high-impact AI use cases, and translate business needs into actionable technical solutions.
- **Lead Office Hours & Hackathons**: Own and facilitate recurring AI office hours for drop-in support and guidance. Design and lead company-wide AI hackathons that inspire experimentation, knowledge sharing, and the discovery of innovative use cases.
- **AI Tool Configuration & Evaluation**: Evaluate, configure, and recommend the right AI tools and platforms for the organization. Stay on top of the rapidly evolving AI tooling landscape and make informed build-vs-buy recommendations.
- **Build Bespoke AI Solutions**: Design, develop, and deploy custom AI solutions tailored to specific team needs—leveraging **LLMs**, **RAG architectures**, agentic workflows, and automation frameworks to solve real business problems.
- **Create Resources & Artifacts**: Develop reusable playbooks, prompt libraries, workflow templates, how-to guides, and reference architectures that enable teams to self-serve on AI adoption.
- **Templatize AI Workflows**: Architect and implement standardized, templatizable AI workflows that can be adapted and deployed across multiple teams and use cases, driving consistency and scale.
- **Training & Teaching**: Lead hands-on training sessions, workshops, and demos to teach teams how to effectively use AI tools and integrate them into their day-to-day work. Act as a force multiplier by building AI fluency organization-wide.
- **Cross-Functional Collaboration**: Partner closely with Engineers, Business Systems, Security, Data Engineering, and other AI Solutions Engineers to integrate AI technologies into existing systems, ensuring security, compliance, and scalability.
- **Strategic Planning & Roadmapping**: Contribute to the AI strategy and roadmap at the organizational level, aligning initiatives with business priorities and ensuring AI investments deliver maximum value.
- **Communicate & Evangelize**: Present findings, demo solutions, share progress updates, and advocate for AI adoption with stakeholders ranging from individual contributors to executive leadership.

## What you’ll bring:

- Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. A graduate degree is a plus.
- **5-7+ years** of experience in engineering, solutions engineering, or technical enablement roles, with a track record of deploying solutions that measurably enhance productivity.
- **4+ years** of hands-on experience developing AI-centric products and solutions, including machine learning, generative AI, **LLMs**, **RAG architectures**, agentic frameworks, and prompt engineering.
- Deep proficiency in **Python** and experience with AI frameworks and tools such as **LangChain**, **LangGraph**, **LlamaIndex**, **OpenAI API**, and similar; familiarity with containerization (**Docker**, **Kubernetes**) and cloud platforms (**AWS**, **GCP**, **Azure**).
- Demonstrated ability to lead enablement programs—including running office hours, hackathons, training sessions, and creating scalable documentation and resources.
- Proven experience gathering requirements from non-technical stakeholders and translating them into well-scoped technical solutions.
- Exceptional ability to tackle open-ended, ambiguous problems in unstructured environments, synthesizing complex information into actionable plans and deliverables.
- Strong communication and presentation skills, with the ability to make complex AI concepts accessible to diverse audiences—from engineers to executives.
- A builder’s mindset: you are equally comfortable architecting a solution, writing production code, creating a training deck, and facilitating a workshop.
- A deep passion for AI and a genuine desire to help others harness its potential to transform how they work.

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