Overview As an Engineering Manager, you lead GenAI and full-stack engineering teams to deliver scalable, secure AI-powered applications at enterprise scale. You guide end-to-end delivery, aligning with technology strategy, governance, and risk controls. You'll collaborate with product, data science, and architecture to translate requirements into robust solutions and foster a culture of innovation and accountability. This role offers exposure to enterprise-wide AI initiatives and opportunity to shape architecture and delivery practices.
Compensation / Benefits- Health benefits
- 401(k) plan
- Paid time off
- Parental leave
- Tuition reimbursement
- Commuter benefits
Responsibilities- Manage and develop a team delivering GenAI and full-stack solutions
- Lead execution of complex technology initiatives including enterprise-wide AI programs
- Oversee design and delivery of Agentic AI systems and automation workflows
- Drive development of end-to-end full-stack solutions (UI, APIs, cloud platforms)
- Partner with architects to ensure adherence to enterprise standards and best practices
- Collaborate with product, business, and data science teams to translate requirements into solutions
- Ensure quality through CI/CD, testing, and observability
- Manage resource allocation, project planning, and delivery timelines
- Mentor and coach engineers including hiring and performance management
- Ensure solutions comply with security, risk, and regulatory requirements
Key requirements- 5+ years of Software Engineering experience
- 2+ years of Leadership experience
- 2+ years of experience in full-stack development (Java, Node.js, Python)
- 1+ year of experience delivering cloud-native applications (GCP, AWS, or Azure)
- 1+ year of experience leading complex technology initiatives
- 1+ year of experience managing engineering teams
- Ability to influence stakeholders and drive cross-functional alignment
- Collaborative leadership and team mentoring
- Strong communication and coordination across product, business, and data science teams
- GenAI, LLMs, or conversational AI
- Agentic AI frameworks and multi-agent architectures
- RAG, prompt engineering, or AI evaluation frameworks
