Manager, Software Engineering, Content Automations

Role Overview

Manager, Software Engineering (Content Automations) at Cengage. You will lead a software engineering team building content automation and AI-first solutions to improve efficiency and quality in content creation workflows.

What You’ll Do

• Partner with content teams to analyze current workflows, identify automation opportunities, and design AI-first solutions.
• Lead end-to-end delivery of automation initiatives across onsite and offshore engineering teams and cross-functional partners.
• Drive development of AI-powered tools via POCs (prototyping), enhancements, and continuous improvement of existing platforms.
• Collaborate with Product Managers and business partners to deliver scalable, innovative solutions aligned with engineering guidelines.
• Act as a technical lead following the Product Operating Model (POM), working with product and design teams to improve customer outcomes.
• Lead, coach, and grow a high-performing engineering team with a focus on quality, accountability, and continuous improvement.
• Reimagine and optimize content creation workflows using modern platforms, automation, and generative AI.
• Participate in POM Discovery activities through ongoing internal/external customer engagement to derive solutions.
• Track quality and efficiency improvements using well-instrumented SDLC tools and report progress to executive teams.
• Balance technical debt/maintenance with new feature development within each sprint.
• Help establish and manage product backlogs, estimates, and budgets with partners and customers.
• Stay ahead of emerging technologies and industry trends to inform product roadmaps.

Skills / Requirements

• Bachelor’s degree in Computer Science (or related) or equivalent education and recent relevant experience.
• 3+ years of technology leadership experience, including:
  • developing technology strategies
  • fostering innovation
  • leading globally distributed teams
• Solid generative AI knowledge including:
  • Machine learning and Language/Reasoning models (including multimodal)
  • Prompt & context engineering
  • agent frameworks and tools
  • RAG and vector databases
• Hands-on experience integrating/experimenting with commercial and open-source GenAI toolkits (e.g., coding assistants, OpenAI Codex, LangChain).
• Experience modernizing legacy/monolithic architectures and migrating toward modular cloud-native platforms (details truncated in posting).