This role is remote and open for both U.S and Canada.

Responsibilities:

1.Technical Leadership:

• Set and evolve the technical direction for the team, ensuring architectural decisions align with product goals, scalability requirements, and industry best practices.

• Champion the adoption of GenAI-powered developer tooling (e.g., GitHub Copilot, Claude Code) to accelerate delivery and improve code quality.

• Conduct and guide rigorous code reviews, architecture discussions, and design sessions to maintain high engineering standards.

• Stay current with emerging technologies, frameworks, and AI/ML advancements relevant to enterprise SaaS development.

• Has full accountability for technical output within the group, ensuring solutions are scalable, secure, and maintainable.

2.Team Leadership and People Development:

• Lead, mentor, and grow a team of 10-15 engineers of varying experience levels, including senior engineers and individual contributors.

• Own the full talent lifecycle: hiring, onboarding, performance management, career development, compensation planning, and retention

• Foster a culture of technical curiosity and continuous improvement, where engineers feel empowered to experiment with new technologies and tools, including AI-assisted workflows.

• Identify skills gaps and build targeted development plans, advocate for team members’ growth and promotion opportunities.

• Responsible for a major engineering function, demonstrating strong leadership and management skills with the ability to manage mid-sized teams.

3.Delivery and Execution:

The role’s core accountability is ensuring the team delivers high-quality, reliable software that meets enterprise-grade reliability and compliance requirements.

• Partner with Product Management and Design to translate business requirements into well-scoped engineering deliverables.

• Own sprint planning, capacity management, and roadmap execution, balancing feature velocity with technical debt reduction.

• Drive a culture of engineering rigor: automated testing, CI/CD maturity, observability, and incident response.

• Define, track, and report on delivery metrics (cycle time, defect rates, deployment frequency) using data-driven approaches.

• Manage competing priorities with transparency, proactively communicating risks and trade-offs to stakeholders.

• Develops operational plans for the group based on broader engineering and corporate strategy; strategic decisions have a 2-4 year impact horizon.

4.GenAI Adoption and Developer Productivity:

• Lead by example in the evaluation, piloting, and scaled rollout of generative AI tools for code generation, test automation, documentation, and developer workflows.

• Define best practices and guardrails for responsible GenAI use within the software development lifecycle (SDLC), including security, IP, and compliance considerations.

• Partner with platform and DevOps teams to integrate AI tooling into CI/CD pipelines and developer environments.

• Measure and communicate the ROI of GenAI adoption through productivity metrics and developer experience surveys.

• Act as an internal evangelist for AI-augmented engineering, educating teams, leadership, and stakeholders on capabilities and responsible use.

5.Cross-Functional Collaboration and Stakeholder Engagement:

• Serve as the primary engineering point of contact for product, design, security, and customer success teams.

• Collaborate with architects, platform engineering, and other group managers to drive consistency in technology choices, shared services, and reusable components.

• Engage with customers and partners when needed to understand technical requirements and provide confidence in engineering capabilities.

• Represent the engineering team in executive reviews, quarterly planning, and cross-functional forums.

6.Security, Quality, and Compliance:

• Ensure software development practices comply with enterprise security standards and relevant regulatory requirements (e.g., SOC 2, HIPAA where applicable).

• Instill quality engineering practices: code coverage standards, static analysis, security scanning, and vulnerability management within the SDLC.

• Partner with QA and security teams to define and enforce quality gates across the delivery pipeline.

Core Competencies:

• Proven ability to lead and grow high-performing software engineering teams in an enterprise SaaS environment.

• Deep technical credibility. Engineers trust your judgment on architecture, tooling, and design decisions.

• Demonstrated hands-on experience adopting and championing generative AI tools within a software development team.

• Strong product mindset with the ability to align engineering work to business outcomes and customer value.

• Exceptional communication skills: able to articulate technical complexity to non-technical audiences and vice versa.

• Data-driven decision maker with experience using engineering metrics to drive continuous improvement.

• Collaborative leader who thrives in cross-functional, fast-paced environments with shifting priorities.

Technical Skills:

• Proficiency in one or more enterprise-grade programming languages and frameworks (e.g., Java, Python, Node.js, React, Go).

• Hands-on experience with generative AI coding tools (GitHub Copilot, Claude Code) and AI-assisted testing/documentation pipelines.

• Solid understanding of cloud-native development on AWS, Azure, or GCP; familiarity with containerization (Docker, Kubernetes) and serverless architectures.

• Experience with CI/CD pipelines, DevOps practices, and modern build/deploy tooling (GitHub Actions, Jenkins, ArgoCD, etc.).

• Familiarity with microservices architecture, API design (REST, GraphQL), and distributed systems patterns.

• Working knowledge of security best practices within the SDLC, including OWASP, SAST/DAST tooling, and secrets management.

• Proficient with engineering productivity and observability tooling (Jira, Datadog, Splunk, PagerDuty, or equivalents).

Supervisory Skills:

• Experienced people manager with a track record of developing engineers at all levels, including senior and staff-level contributors.

• Skilled at navigating performance management, compensation conversations, and career pathing in a structured, equitable manner.

• Ability to build team cohesion and maintain morale through periods of organizational change, ambiguity, or high delivery pressure.

• Experience managing in a globally distributed or remote-first engineering environment.

Qualifications Required:

Education Requirements:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field.

• Equivalent practical experience in lieu of formal education will be considered.

Experience:

• 10+ years of software engineering experience, including 5+ years in an engineering management or technical lead role.

• 5+ years of experience managing teams of 8 or more engineers in an enterprise SaaS company.

• Demonstrated experience driving the adoption of generative AI tooling within a software development team.

• Background in building and shipping enterprise software products with high reliability, security, and scalability requirements.

• Experience working in regulated or compliance-sensitive industries (healthcare IT, fintech, or similar) is a plus.

Travel:

Up to 15%

This role offers a compelling opportunity to shape how a world-class engineering team builds and ships software in the age of AI. If you are a technical leader who combines a passion for