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About the role
About the Job We are looking for a Director of Engineering to own our engineering organization and drive the execution of our enterprise-grade AI solutions. This is a pure execution and delivery role. You will bridge executive strategy with hands-on engineering execution, managing Engineering Managers and Tech Leads to ensure our AI systems ship on schedule, perform reliably, and adhere to quality benchmarks. If you thrive on building high-throughput teams, solving non-deterministic AI challenge pipelines, and creating operational discipline, this role is for you. Key responsibilities Delivery Predictability & Execution - Own the Release Schedule: Turn long-term product roadmaps into predictable sprint cycles, hitting an 85%+ on-time delivery rate across all engineering teams. - Remove Roadblocks: Proactively detect and resolve cross-team dependencies, architectural bottlenecks, and resource constraints before they impact deadlines. - Process Discipline: Oversee modern CI/CD, automated testing, and agile workflows across all sub-teams to support continuous, low-risk deployments. AI Quality & Governance - Production Evaluation Suites: Establish automated evaluation pipelines (evals) to test AI outputs for hallucination rates, relevance, groundedness, and context accuracy before code hits production. - Safety & Guardrails: Implement circuit breakers, validation filters, and human-in-the-loop fallbacks to maintain system integrity when underlying LLMs produce unexpected responses. - Data & RAG Pipeline Excellence: Maintain vector stores, data ingestion feeds, and retrieval mechanisms to ensure the AI consumes clean, structured, and compliant context. Architecture & System Reliability - High Availability: Oversee application architecture to guarantee targeted Service Level Objectives (SLOs), including 99.9% uptime and low time-to-first-token (TTFT) latency. - Resilient Infrastructure: Partner with Staff and Principal Engineers to design resilient fallback logic when model APIs drop or experience rate limits. - Technical Standards: Maintain high standards across code review, observability, automated integration testing, and security compliance. Engineering Culture & Organizational Growth - Manager of Managers: Directly manage, mentor, and elevate Engineering Managers and Tech Leads, fostering autonomy and clear accountability. - Talent Acquisition: Attract, interview, and hire top-tier software and AI engineers to scale the department efficiently. - High-Performance Culture: Cultivate an engineering workplace grounded in technical ownership, continuous improvement, and operational rigor. Definitive Expectations (What Success Looks Like) First 30 Days Audit existing delivery pipelines, establish baseline AI evaluation criteria, and assume direct management of Engineering Managers/Leads First 60 Days Implement automated AI eval frameworks, streamline cross-team dependencies, and establish reliable release cadences. First 90 Days Achieve >85% sprint commitment completion , decrease production AI quality regressions, and stabilize system latency metrics. Qualifications & Requirements - Experience: 14+ years in software engineering with 5+ years managing Engineering Managers or Tech Leads in high-growth or enterprise settings. - AI/ML Technical Literacy: Hands-on experience on delivering AI systems (LLMs, RAG architectures, vector databases, prompt engineering, or fine-tuning workflows). - Delivery Track Record: Proven experience leading multi-team initiatives from technical concept through launch on predictable schedules. - Systems Architecture Knowledge: Experience building distributed systems, cloud-native backend infrastructure (AWS/GCP/Azure), and scalable APIs. - Leadership Focus: Strong ability to hold teams accountable to hard deadlines without sacrificing quality or developer morale. .
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