Engineering Director - AI Solutions & Delivery

Role : Director of Engineering (AI Solutions & Delivery) :

About The Role :

We are looking for a Director of Engineering to lead engineering execution and delivery across our enterprise AI solutions.

This role combines strong engineering leadership with operational discipline, ensuring AI products move from technical design to production with predictable timelines, high quality, and reliable performance.

You will lead Engineering Managers and Tech Leads, oversee multiple engineering teams, and drive delivery excellence across AI-powered applications and platforms.

The focus is on execution, technical quality, and organizational accountability, ensuring teams deliver scalable, production-ready AI systems without compromising reliability or engineering standards.

Key Responsibilities :

Engineering Delivery & Execution :

- Own engineering delivery schedules, translating product roadmaps into achievable sprint plans and release milestones.

- Establish measurable delivery targets, aiming for at least 85% on-time delivery across engineering teams.

- Identify and resolve cross-team dependencies, technical bottlenecks, and resource constraints before they affect delivery.

- Drive engineering process improvements across Agile development, CI/CD, automated testing, and release management.

- Monitor delivery metrics, sprint commitments, and release readiness to maintain predictable execution.

AI Quality, Evaluation & Governance :

- Establish automated AI evaluation pipelines to measure hallucinations, relevance, groundedness, and contextual accuracy before production releases.

- Define quality benchmarks and evaluation criteria for LLM-powered applications and AI-driven workflows.

- Implement safety mechanisms, validation filters, circuit breakers, and human-in-the-loop fallbacks to manage unpredictable AI outputs.

- Oversee RAG pipelines, vector databases, data ingestion, and retrieval mechanisms to maintain accurate, reliable, and compliant context.

- Reduce production AI quality regressions through continuous evaluation, monitoring, and feedback loops.

Architecture, Scalability & Reliability :

- Oversee the architecture of distributed systems and cloud-native applications to support scalability, availability, and performance.

- Drive reliability targets, including 99.9% uptime and low time-to-first-token (TTFT) latency for applicable AI applications.

- Partner with Staff and Principal Engineers to design resilient systems, including fallback mechanisms for model API failures, timeouts, and rate limits.

- Establish engineering standards for code reviews, observability, automated integration testing, and security compliance.

- Ensure production systems meet defined service-level objectives (SLOs) and performance benchmarks.

Engineering Leadership & Team Development :

- Lead and develop Engineering Managers and Tech Leads, establishing clear ownership, accountability, and delivery expectations.

- Build a high-performance engineering culture focused on technical excellence, collaboration, and continuous improvement.

- Partner with Talent Acquisition to attract, assess, and hire strong software engineering and AI talent.

- Align engineering resources with business priorities, delivery commitments, and technical requirements.

- Address performance and execution challenges while maintaining team engagement and engineering quality.

Success Metrics : First 90 Days :

1. First 30 Days :

- Assess existing engineering workflows, delivery pipelines, and release processes.

- Establish baseline AI evaluation criteria and identify gaps in production quality.

- Take ownership of engineering delivery across teams and align expectations with Engineering Managers and Tech Leads.

2. First 60 Days :

- Implement automated AI evaluation frameworks and establish measurable quality benchmarks.

- Improve cross-team coordination and resolve key delivery dependencies.

- Establish consistent sprint planning, release cadences, and delivery reporting.

3. First 90 Days :

- Achieve at least 85% sprint commitment completion.

- Demonstrate measurable reductions in production AI quality regressions.

- Improve system reliability and stabilize latency against defined performance targets.

- Establish clear accountability and predictable delivery practices across engineering teams.

Qualifications & Requirements :

- Experience : 14+ years in software engineering, including at least 5 years managing Engineering Managers or Tech Leads in enterprise or high-growth environments.

- AI/ML Expertise : Hands-on experience delivering AI-powered systems involving LLMs, RAG architectures, vector databases, prompt engineering, or model fine-tuning workflows.

- Engineering Delivery : Proven ability to lead multiple engineering teams and deliver complex technical initiatives from design through production launch.

- System Architecture : Experience with distributed systems, cloud-native backend infrastructure, and scalable APIs across AWS, GCP, or Azure.

- AI Quality & Reliability : Understanding of AI evaluation frameworks, production monitoring, hallucination mitigation, guardrails, and system reliability.

- Delivery Management : Strong experience with Agile methodologies, CI/CD, release planning, and engineering performance metrics.

- Leadership : Ability to establish accountability, manage competing priorities, and deliver against ambitious deadlines without compromising quality or team morale.

What Success Looks Like :

The successful candidate will establish predictable engineering delivery, improve the quality and reliability of production AI systems, and build a culture of ownership across engineering teams.

They will combine technical judgment with execution-focused leadership to ensure AI solutions are delivered on schedule, operate reliably, and meet enterprise expectations.

If you are passionate about building high-performing engineering teams and delivering enterprise-grade AI solutions at scale, we would like to hear from you.

Skills : Artificial Intelligence (AI), Anthropic Claude and Engineering Management.

Skills

Engineering Management, Artificial Intelligence, Generative AI, Machine Learning, System Architecture, Solution Design, Delivery Management, LLM, RAG, AWS