Job DescriptionPenguin Random House publishes more of the books people love than anyone else in the world and the Data Science team helps those books find their readers: recommendation systems that surface the right book for the right person, forecasting models that guide print runs and marketing spend, and AI-powered tools that support our publishing teams.
We're hiring a Senior AI Solutions Engineer to build the systems behind this next wave of AI work for improving operations: agentic applications, LLM-powered services, and the interfaces (e.g. MCP servers) that connect AI models and assistants to our internal data and tools. You'll work at the intersection of a world-class publishing business and the modern AI stack, and you'll own what you build all the way to production.
Specific responsibilities include:- Design, build, and operate LLM-powered applications and services (e.g. agentic workflows, retrieval/RAG systems, classification pipelines) over our catalog, sales, and operational data
- Stand up and maintain MCP servers and tool integrations that connect AI models and assistants to our internal data and systems safely and reliably
- Own services end-to-end: architecture, implementation, deployment, evaluation, and monitoring in our AWS/Databricks environment
- Evaluate emerging AI tooling and patterns (agent frameworks, MCP, evaluation harnesses, new model capabilities), run structured pilots, and lead adoption of what works
- Raise the team's AI engineering capability: run working sessions and internal demos, pair program with scientists, and build internal tools to help accelerate AI usage across the team.
Qualifications- 3+ years building and shipping production-quality software, including hands-on work with machine learning or AI systems that include the following preferred qualifications:
- Experience standing up MCP servers or comparable tool-integration layers for AI assistants
- Experience with cloud ML platforms (AWS, Databricks), model APIs (Bedrock, Anthropic, OpenAI), and vector databases
- Advanced degree (MS/PhD) in computer science, machine learning, or a related quantitative field
- Experience applying AI/ML to commercial problems such as demand forecasting, pricing, sales and marketing optimization, recommendation, or search
- Background in media, publishing, or other content-rich domains
- Demonstrated fluency with the current LLM stack: retrieval-augmented generation, embeddings, prompt engineering, agentic patterns, and rigorous evaluation of generative systems
- Strong Python engineering skills with experience in version control, testing, and CI
- Self-motivated, with strong communication skills and a demonstrated ability to teach and level up teammates.
Additional InformationThe salary range for this position is $180,000 to $220,000. All positions are currently eligible for annual profit award or bonus, subject to Company results. Applications for this role will be accepted through October 13, 2026 or until the role is filled. We encourage you to apply early, as we review applications on a rolling basis. For this position a complete application should include your resume and cover letter, as both are required for consideration. Before applying for any role at Penguin Random House, we recommend you review our applicant resources pageand our FAQs page.Penguin Random House job postings include a good faith compensation range for each open position. The salary range listed is specific to each particular open position and takes into account various factors including the specifics of the individual role, and candidate's relevant experience and qualifications.
Full-time employees are eligible for our comprehensive benefits program. Our range of benefits include, but are not limited to, Medical/Prescription drug insurance, Dental, Vision, Health Care/Dependent Care Flexible Spending Account, Health Savings Account, Pre-Tax and Roth 401(k), Short and Long-Term Disability Insurance, Life/AD&D Insurance, Commuter Benefits, Student Loan Repayment Program, Educational Assistance & generous paid time off.
