Principal AI & Engineering Productivity Lead

Job Qualifications

The applicant or candidate must have/be:

Technical & Engineering Expertise

• 10+ years of software development experience, with strong expertise in Java; familiarity with modern frontend frameworks (React, etc.) is a plus
• Hands-on experience with SaaS product development and building production-ready AI systems
• Proven experience with LLMs, AI agents, and tools like Claude, Kiro, GitHub Copilot, or similar
• Strong understanding of modern software architecture, scalable systems, and AI integration in real-world workflows

AI & R&D Experience

• Implemented AI-driven solutions in production systems, including LLM orchestration, prompt engineering, retrieval-augmented systems, and agent-based workflows
• Skilled in evaluating new AI technologies pragmatically and translating prototypes into scalable solutions
• Strong research and experimentation mindset with focus on delivering measurable impact

KEY RESPONSIBILITIES

1) AI-Driven Engineering Acceleration (Primary Focus)

• Deliver measurable improvements in engineering productivity by designing and operationalizing AI-enabled workflows
• Drive successful adoption of enterprise AI tools or equivalent AI coding/productivity tools (e.g., Claude, Kiro, GitHub Copilot, agent-based systems) to create tangible gains in speed, quality, and automation
• Build and scale internal AI assistants that reduce development effort across code generation, refactoring, documentation, test creation, and debugging
• Achieve sustained improvements in:

> Code quality and maintainability standards

> Automated test coverage and defect reduction

> Documentation accuracy and accessibility

> API design consistency and reusability

> Deployment efficiency and release reliability

• Establish governance frameworks and best practices that ensure secure, responsible, and scalable AI adoption
• Enable engineers to confidently integrate AI into daily workflows through structured coaching, practical playbooks, and hands-on guidance

2) Product AI Innovation (Secondary Focus)

• Identify and prioritize high-impact opportunities to embed AI into Strato product features that enhance customer value and competitive differentiation
• Rapidly prototype and validate AI-driven capabilities, converting viable concepts into production-ready solutions
• Partner cross-functionally with Product, UX, and Engineering to accelerate the commercialization of validated AI innovations
• Deliver AI features that are practical, scalable, secure, and commercially viable—driving measurable customer adoption and business growth

3) AI Architecture Foundations

• Define and operationalize a long-term AI architecture strategy that supports scalable, secure, and enterprise-grade AI adoption across Strato
• Architect and validate robust AI patterns, including LLM integrations, agent-based systems, retrieval-augmented generation (RAG), internal knowledge systems, and vector database strategies where appropriate
• Establish governance frameworks that ensure secure, compliant, and responsible AI usage aligned with data privacy and enterprise standards
• Ensure AI-enabled components are scalable, maintainable, observable, and production-ready—supporting long-term sustainability and technical resilience

4) AI Culture & Education

• Build and institutionalize AI capability across engineering through structured education initiatives and practical enablement programs
• Deliver high-impact workshops and hands-on sessions that accelerate confident, effective AI adoption in daily engineering workflows
• Foster a culture of experimentation, continuous learning, and responsible AI usage grounded in governance and best practices
• Establish yourself as the trusted AI thought leader within Strato Engineering, providing strategic guidance and technical direction on AI initiatives