How Remoteville checks and expires listings
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor’s degree in Computer Science or related field or equivalent experience
- 3+ years of technology leadership experience
- Experience developing technology strategies and fostering innovation
- Experience leading globally distributed teams
- Working knowledge of AI concepts including machine learning LLMs multimodal models prompt engineering AI agents RAG and vector databases
- Hands-on experience with commercial or open-source GenAI tools such as OpenAI Hugging Face or LangChain
- Experience modernizing legacy or monolithic architectures
- Experience migrating systems to modular cloud-native platforms and hybrid cloud strategies
- Ability to design and develop APIs integrating legacy systems with modern solutions
- Experience deploying solutions on AWS GCP or Azure including containers serverless and cloud storage or compute services
- Proven ability to build secure resilient scalable high-availability enterprise systems
- Strong experience with agile delivery such as Scrum Kanban or SAFe
- Experience with Jira or Azure DevOps
- Demonstrated success organizing teams around a Product Operating Model
- Strong cross-functional collaboration in distributed remote-first environments
- Ability to communicate complex technical concepts to technical and non-technical audiences including executives
Nice to have
- Innovative
- Collaborative
- Strategic
- Customer-focused
- Mentoring mindset
- Data-driven
Day to day
- Lead a software engineering team focused on building tools that deliver world-class content for Cengage Learning Platforms.
- Define and execute an AI-first roadmap that strengthens content quality and accessibility while improving customer outcomes.
- Collaborate closely with product and design partners using the Product Operating Model to shape discovery, backlogs, estimates, and delivery priorities.
- Drive continuous improvement through modern automation data-driven insights and well-instrumented SDLC metrics.
- Balance technical debt maintenance and new feature development while mentoring the team and communicating progress to executives.