Job Description
Job Description
Seeking a Head of Engineering to oversee all technical operations, software development, and infrastructure for an AI start up. This role is responsible for the technical integrity of the platform, the productivity of the engineering team, and the alignment of technical execution with business strategy. As a senior leader, you are expected to identify technical risks, manage the development lifecycle, and provide candid, data-driven feedback on product feasibility and resource allocation.
Core Responsibilities
1. AI Strategy & Technical Leadership
- AI-Native Architecture: Own the long-term technical roadmap with a focus on AI-native product design. Lead the transition from simple LLM integration to sophisticated, multi-agentic systems.
- Agentic Coding & Workflows: Oversee the implementation of agentic coding patterns and autonomous workflows that enable our AI to function as an end-to-end "Workshop OS."
- Architectural Oversight: Make high-stakes decisions on system architecture, tech stack evolution, and the integration of vector databases, orchestration frameworks, and evaluation layers.
- Strategic Input: Act as the primary technical advisor to the CEO. Provide rigorous pushback on product requirements that compromise system stability or long-term scalability.
- Risk Management: Proactively identify and surface technical debt, security vulnerabilities, and infrastructure bottlenecks. Develop mitigation plans before these issues impact the customer experience.
- Buy vs. Build: Lead the evaluation of external AI models and tools versus internal development to optimize for both performance and cost-efficiency.
2. Engineering Operations
- SDLC Management: Standardize and optimize the Software Development Life Cycle, including sprint planning, code reviews, and automated testing for non-deterministic AI outputs.
- Deployment & Velocity: Own the CI/CD pipeline. Responsible for maintaining a high deployment frequency while ensuring zero-downtime and system reliability.
- Security & AI Ethics: Ensure all development meets industry standards for data privacy and security, specifically concerning RAG (Retrieval-Augmented Generation) and agentic data handling.
3. Team Management & Talent
- Hiring & Retention: Lead the recruitment of high-caliber engineering talent with specific expertise in AI/ML and modern software patterns.
- Performance Management: Set clear KPIs for the engineering team. Conduct regular performance reviews and address underperformance directly and swiftly.
- Mentorship: Foster a culture of continuous learning and technical rigor, particularly regarding emerging AI technologies and agentic frameworks.
Qualifications & Required Skills
- Experience: Minimum of 8 years in software engineering, with at least 3 years in a formal leadership role.
- AI Expertise: Proven track record of building and scaling AI-native products. Deep familiarity with LLM orchestration (e.g., LangChain, AutoGen), vector stores, and agentic design patterns.
- Technical Proficiency: Expert-level understanding of full-stack architecture, cloud infrastructure (GCP preferred), and the unique challenges of testing and monitoring agentic systems.
- Leadership Style: Demonstrated ability to lead with high autonomy. Experience in environments that value direct communication and critical "pushback" on strategic decisions.
- Communication: Ability to translate complex technical constraints into clear business risks and opportunities for non-technical stakeholders.
Key Performance Indicators
- System Uptime & Stability: Maintaining 99.9% availability of the AI platform.
- Model Performance: Monitoring and optimizing for latency, cost, and accuracy of AI-driven features.
- Feature Velocity: Consistency in meeting sprint goals and product roadmap milestones.
- Resource Efficiency: Optimization of infrastructure spend and token usage/API costs.
Hybrid Schedule
- In the Office (Cherry Creek) Tue, Wed, Thurs
- Remote ok Mon, Fri