About the job
411 Locals is seeking a Director of Applied AI & Automation to own the end-to-end delivery of practical, production-grade AI and automation solutions across the company.
This is not a research, innovation lab, or experimentation role. This leader is responsible for turning business problems into working systems. Owning IT Development, Business Analysis, and Development QA as one integrated delivery engine, ensuring AI and automation initiatives ship, scale, and stay reliable, and converting IT from a cost center into a measurable operational and revenue accelerator.
The ideal candidate is a builder-leader: someone who understands strategy but is most comfortable shipping systems, enforcing discipline, and holding teams accountable to outcomes.
Qualifications:
Required
- 8–12+ years in software engineering, automation, and/or applied AI roles
- 5+ years leading cross-functional delivery teams (Dev + BA + QA)
- Proven experience shipping production AI or automation systems
- Strong understanding of:
- System architecture
- APIs and integrations
- Automation frameworks
- AI/ML implementation (applied, not academic)
- Demonstrated ability to:
- Translate business needs into working systems
- Drive delivery under real operational constraints
- Experience supporting digital marketing, sales, or call-center ecosystems is a strong plus
Preferred
- Experience implementing ITIL-aligned operational practices (practical, not theoretical)
- Strong financial and ROI mindset
- Experience with cloud platforms and scalable automation
- Comfort working in high-growth, fast-changing environments
Key Responsibilities:
1. Own Applied AI & Automation Delivery (End-to-End)
- Lead the design, build, deployment, and maintenance of AI-driven and automation systems used in real production environments.
- Ensure every initiative has:
- A clear business owner
- Measurable KPIs
- Defined success and failure criteria
- Eliminate “proof-of-concept purgatory” — systems must work in production or be shut down.
2. Unified Ownership of IT Development, Business Analysis & QA
- Consolidate Development, Business Analysis, and QA into a single execution pipeline.
- Ensure:
- Business requirements are clearly translated into technical specs
- QA is embedded early (not after-the-fact)
- Automation and AI systems are testable, observable, and supportable
- Enforce disciplined SDLC practices with speed and accountability, not bureaucracy.
3. Business-Driven Automation Strategy
- Work directly with business leaders to identify:
- Manual workflows
- Bottlenecks
- High-error, high-cost processes
- Prioritize automation initiatives based on:
- ROI
- Risk reduction
- Revenue enablement
- Translate business pain into automated workflows, AI decision systems, and scalable tools.
4. Operational Excellence & Reliability
- Implement standards for:
- System reliability
- Monitoring and alerting
- Incident response
- Regression testing for AI-driven systems
- Reduce downtime, rework, and system fragility.
- Own post-deployment accountability — delivery does not end at launch.
5. AI That Delivers Value (Not Hype)
- Apply AI where it materially improves outcomes, including:
- Workflow automation
- Classification and decision support
- QA and auditing automation
- Customer operations, sales, and marketing systems
- Evaluate AI tools pragmatically:
- Build when needed
- Buy when faster
- Kill when ineffective
6. Governance, Metrics & Accountability
- Establish clear governance for:
- AI usage
- Automation logic
- Data quality
- Change management
- Define and track KPIs across:
- Delivery speed
- Automation coverage
- Error reduction
- Cost savings
- Revenue enablement
- Communicate progress with facts, metrics, and outcomes, not narratives.
7. Team Leadership & Capability Building
- Lead and develop:
- Developers
- Business Analysts
- QA Engineers
- Build teams that:
- Understand the business
- Own their systems
- Think in outcomes, not tickets
- Create a culture of ownership, clarity, and execution.
Key Performance Indicators (KPIs):
- Automation Impact: Reduction in manual effort, errors, and cycle time
- Delivery Velocity: Time from requirement to production
- System Reliability: Uptime, incident frequency, regression rates
- Business Value: Cost savings and revenue enablement from AI/automation
- Team Effectiveness: Retention, skill growth, delivery consistency