Senior Engineering Manager - SaaS Platform

Ideal Candidate Profile:

A hands-on technical leader with strong architectural depth and a track record of scaling SaaS platforms. You bring a blend of Engineering rigor, People leadership, and Strategic thinking. You are comfortable operating in a global matrix organization and can influence cross-functional stakeholders. You are passionate about modern architecture, AI enablement, and building high-performing teams.

Required Experience & Qualifications:

- 12+ years of software engineering experience with 3+ years in engineering leadership roles.

- Strong expertise in .NET, .NET Core, ASP.NET, C#, and Python.

- Proven experience designing distributed, scalable SaaS platforms.

- Deep understanding of Azure cloud infrastructure and services.

- Experience with RESTful APIs, microservices, containerization (Docker), and CI/CD pipelines.

- Strong knowledge of relational (SQL Server, PostgreSQL) and NoSQL databases.

- Experience working in product-based or SaaS organizations.

- Experience working on or scaling a Content Management System (CMS) platform is a strong advantage.

- Demonstrated ability to lead globally distributed teams.

- Strong stakeholder management and executive communication skills.

Key Focus Areas:

- CMS platform scalability and modernization

- AI/LLM integration into content workflows

- Engineering productivity acceleration

- Cloud cost optimization

- Reliability engineering and observability

- Talent development within GCC

- Secure and compliant architecture

Capabilities:

1. Technical Capabilities:

- Cloud-native architecture design

- Microservices and distributed systems

- DevOps and CI/CD automation

- Performance tuning and scalability

- AI/ML integration exposure

- Secure coding and compliance frameworks

2. Leadership Capabilities:

- Strategic thinking

- Decision-making under ambiguity

- Influencing without authority

- Coaching and mentoring

- Execution excellence

- Global collaboration

KPI and Metrics:

- Platform uptime and SLA adherence

- Release predictability and on-time delivery

- Engineering velocity and sprint predictability

- Defect density and quality metrics

- Cloud infrastructure cost optimization

- Team engagement and retention metrics

- Innovation initiatives implemented (AI/automation adoption)

Qualifications:

- B. Tech / M. Tech in Computer Science or related discipline.

- Certifications in Azure (preferred).

- Exposure to AI frameworks and LLM platforms (preferred).