Director, Mitratech Catalyst

Mitratech is looking for a Director, Mitratech Catalyst — a hands-on technical leader who will launch new applications from zero to one using AI-augmented development practices and rapid delivery cycles. This role sits at the intersection of product engineering and organizational transformation: you will conceive, architect, and ship new products in compressed 8–12 week sprints, then hand them off to divisional engineering teams equipped to carry the work forward.

You will operate as the tip of the spear for Mitratech’s next generation of product development, proving that AI-native tooling and modern delivery practices can dramatically compress time-to-value. Success in this role is measured equally by what you build and by what others are able to sustain after you move on.

• Lead end-to-end development of new applications and features from concept through production release within 8–12 week project cycles.
• Define architecture, select technology stacks, and make build-vs-buy decisions optimized for speed, quality, and long-term maintainability.
• Leverage AI-assisted development tools (e.g., Claude, Copilot, cursor-based IDEs) to accelerate coding, testing, documentation, and review workflows.
• Personally write production code alongside a small, high-velocity squad — this is not a management-only role.
• Deliver working software that meets Mitratech’s standards for security, scalability, and cloud-native deployment (AWS-first, Terraform IaC).

• Design and execute structured handoff plans so divisional engineering teams can own, extend, and operate each delivered product independently.
• Embed AI-augmented development practices into receiving teams through pairing sessions, documentation, runbooks, and lightweight training.
• Establish repeatable playbooks for the full 0→1 lifecycle: ideation, rapid prototyping, MVP delivery, production hardening, and transition.
• Partner with engineering managers and tech leads to ensure receiving teams have the skills, context, and confidence to iterate post-handoff.

• Champion and evolve Mitratech’s Catalyst delivery model, including rapid sprint cadences and automated quality gates.
• Serve as an internal evangelist for AI-native engineering practices, demonstrating what’s possible and raising the bar across the organization.
• Collaborate cross-functionally with Product, Design, Operations, and divisional Engineering leadership to identify high-impact build opportunities.
• Contribute to Mitratech’s AI governance standards, ensuring all AI-assisted outputs meet policy requirements for data protection, auditability, and customer trust.

• 10+ years of software engineering experience with a track record of shipping production applications at scale.
• Demonstrated experience leading 0→1 product builds — from blank repo to live customers — in fast-paced or startup-like environments.
• Deep fluency with AI-assisted development workflows (LLM-based code generation, AI pair programming, automated testing with AI, prompt engineering for developer productivity).
• Strong architectural instincts across full-stack, cloud-native systems (AWS, containerization, CI/CD, infrastructure-as-code).
• Proven ability to hand off complex systems to other teams with clear documentation, clean codebases, and effective knowledge transfer.
• Experience leading or mentoring engineers and influencing engineering culture without relying on positional authority.

• Experience in B2B SaaS, legal tech, compliance, HR tech, or similarly regulated domains.
• Familiarity with M&A-driven engineering environments where multiple product lines and tech stacks coexist.
• Background in establishing internal platforms, accelerators, or innovation labs.
• Experience working within or alongside DevOps/SRE organizations and platform engineering teams.

• Products shipped: Number of new applications delivered to production within target timelines and quality standards.
• Handoff effectiveness: Receiving teams are independently shipping features and operating the product within 30 days of transition.
• Practice adoption: Measurable uptake of AI-augmented development practices across divisional engineering teams post-engagement.
• Cycle time compression: Demonstrable reduction in time-from-concept-to-production compared to traditional delivery methods.
• Team confidence: Positive feedback from receiving teams on clarity of documentation, code quality, and transition support.