Head - Engineering Management

As the Head of Engineering at a funded AI SaaS company, your role will involve the following key responsibilities: - Technology Leadership: Define and drive the technical vision and strategy, particularly in building scalable, distributed, and high-performance systems. - System Architecture: Lead design discussions and architectural reviews, ensuring systems are robust, secure, and capable of supporting rapid scale. - People Leadership: Manage and grow a team of engineers, including senior/principal engineers; foster a culture of ownership, innovation, and engineering excellence. - Cross-Functional Collaboration: Partner with product, data, and AI/ML teams to align technical execution with business priorities. - Execution & Delivery: Ensure timely, high-quality delivery of critical projects, balancing long-term architecture with short-term business needs. - Mentorship: Coach and develop engineering managers and senior engineers, building leadership capacity within the team. - Innovation: Stay current with advances in cloud, SaaS, AI/ML, and distributed systems; champion adoption of modern technologies and practices where relevant. Qualifications required for this role are as follows: - 8+ years of professional experience in software engineering, with significant time spent in SaaS product companies. - 4+ years of experience in people management, leading teams of engineers and/or engineering managers. - Proven expertise in system design, distributed systems, and cloud-native architectures. - Strong track record of technology leadership, influencing architectural direction, and driving organizational change. - Hands-on ability to evaluate and guide technical choices across programming languages (Python, Node.js, or similar) and cloud platforms (AWS, GCP, Azure). - Excellent leadership, communication, and stakeholder management skills. While not mandatory, the following qualifications are preferred: - Prior experience in high-growth startups or rapidly scaling product organizations. - Exposure to AI/ML systems, LLMs, or agentic architectures. - Contributions to open-source projects or published technical thought leadership. As the Head of Engineering at a funded AI SaaS company, your role will involve the following key responsibilities: - Technology Leadership: Define and drive the technical vision and strategy, particularly in building scalable, distributed, and high-performance systems. - System Architecture: Lead design discussions and architectural reviews, ensuring systems are robust, secure, and capable of supporting rapid scale. - People Leadership: Manage and grow a team of engineers, including senior/principal engineers; foster a culture of ownership, innovation, and engineering excellence. - Cross-Functional Collaboration: Partner with product, data, and AI/ML teams to align technical execution with business priorities. - Execution & Delivery: Ensure timely, high-quality delivery of critical projects, balancing long-term architecture with short-term business needs. - Mentorship: Coach and develop engineering managers and senior engineers, building leadership capacity within the team. - Innovation: Stay current with advances in cloud, SaaS, AI/ML, and distributed systems; champion adoption of modern technologies and practices where relevant. Qualifications required for this role are as follows: - 8+ years of professional experience in software engineering, with significant time spent in SaaS product companies. - 4+ years of experience in people management, leading teams of engineers and/or engineering managers. - Proven expertise in system design, distributed systems, and cloud-native architectures. - Strong track record of technology leadership, influencing architectural direction, and driving organizational change. - Hands-on ability to evaluate and guide technical choices across programming languages (Python, Node.js, or similar) and cloud platforms (AWS, GCP, Azure). - Excellent leadership, communication, and stakeholder management skills. While not mandatory, the following qualifications are preferred: - Prior experience in high-growth startups or rapidly scaling product organizations. - Exposure to AI/ML systems, LLMs, or agentic architectures. - Contributions to open-source projects or published technical thought leadership.