About the Role

Staritas is seeking a Director of Software Engineering to lead, modernize, and scale its engineering organization. This role is responsible for software delivery, team leadership, platform modernization, DevOps maturity, production reliability, software quality, and responsible AI adoption.

The Director will lead engineering managers, technical leads, and software teams building secure, scalable, business-aligned enterprise applications. This leader will partner with Product, Architecture, Data, Security, Infrastructure, Operations, and business stakeholders to turn strategy into clear roadmaps and measurable outcomes.

Key Responsibilities

• Lead, coach, and develop high-performing software engineering teams.
• Own delivery outcomes, including roadmap execution, quality, reliability, team performance, and engineering effectiveness.
• Partner with the CTO and technology leaders on engineering strategy, modernization, AI enablement, SDLC improvements, and delivery operations.
• Work with Product and business stakeholders to prioritize roadmaps, balance feature delivery with technical health, and align work to business goals.
• Establish engineering standards, delivery metrics, agile practices, quality gates, documentation expectations, and release discipline.
• Drive modernization of legacy applications, platforms, development practices, deployment models, and workflows.
• Improve delivery through CI/CD, automated testing, secure development, observability, incident management, and continuous improvement.
• Reduce technical debt, platform fragmentation, manual processes, recurring defects, and production instability.
• Build a culture of accountability, collaboration, ownership, learning, quality, and operational excellence.
• Lead hiring, onboarding, performance management, career development, succession planning, and team health initiatives.

AI Enablement

• Champion responsible AI adoption across software engineering.
• Evaluate and guide use of tools such as Claude, ChatGPT, Gemini, GitHub Copilot, Cursor, and AI-enabled IDEs.
• Help teams use AI for code generation, reviews, refactoring, testing, documentation, troubleshooting, research, and workflow optimization.
• Develop responsible AI standards covering data protection, acceptable use, human review, auditability, IP protection, cost management, and risk mitigation.
• Partner with Architecture, Security, Compliance, Legal, Data, and business stakeholders to ensure AI use aligns with enterprise governance.

Required Qualifications

• Bachelor’s or master’s degree in Computer Science, IT, Engineering, or equivalent experience.
• 12+ years of progressive technology experience.
• 5+ years of engineering leadership experience.
• Proven experience leading software delivery, engineering execution, production reliability, and technical quality.
• Experience hiring, coaching, and developing engineering managers, technical leads, senior engineers, and individual contributors.
• Strong background in enterprise applications, APIs, integrations, cloud platforms, DevOps, CI/CD, automated testing, and production support.
• Knowledge of secure SDLC, agile delivery, observability, release management, databases, cloud-native delivery, cybersecurity, and operational resilience.
• Hands-on experience using AI tools to improve engineering productivity, quality, documentation, testing, or troubleshooting.

Preferred Qualifications

• Experience modernizing enterprise platforms and reducing technical debt.
• Experience with AWS, Azure, Google Cloud, or hybrid cloud environments.
• Experience with SaaS, ERP, CRM, workflow, analytics, data platforms, or reporting systems.
• Familiarity with SAFe, ITIL, Agile, Scrum, Kanban, or similar frameworks.
• Experience implementing engineering operating models, delivery metrics, architecture reviews, reusable patterns, and documentation standards.
• Experience evaluating or implementing AI platforms, LLM integrations, AI agents, RAG, MCP servers, or AI governance practices.

Success Measures

• Engineering teams deliver faster, with higher quality, predictability, and reliability.
• Teams are engaged, accountable, and operating with clear goals.
• Stakeholders have confidence in delivery transparency and execution.
• Engineering standards, DevOps practices, secure development, API patterns, and AI usage guidelines are consistently applied.
• Technical debt, defects, manual effort, and production instability are reduced.
• AI adoption is practical, secure, governed, and measurable.