Role Summary
Own the end‑to‑end technical strategy and execution of Applied AI systems across the enterprise. This role bridges business problems, AI architecture, and production delivery, ensuring AI initiatives are scalable, secure, auditable, and ROI‑driven.
Preferred Qualifications
- 12–15+ years in engineering, with 6+ years in AI/ML systems
- Proven ownership of large‑scale, production AI platforms
- Experience influencing enterprise‑level technology decisions
Key Responsibilities:
- Define and own Applied AI architecture (LLMs, RAG, agents, ML systems) from POC to production.
- Lead design and delivery of enterprise‑grade AI solutions (e.g., AI agents, copilots, decision engines, fraud/risk models).
- Translate ambiguous business problems into clear AI system designs with measurable outcomes.
- Own model selection strategy (open‑source vs proprietary, cost vs performance, cloud vs hybrid).
- Establish AI engineering standards: prompt design, evaluation, observability, fallback, security.
- Drive Responsible AI practices: bias checks, auditability, explainability, data governance.
- Mentor senior and mid‑level engineers; act as technical authority for AI reviews.
- Partner with CTO, Security, Legal, and Business leaders on AI adoption and risk decisions.
- Influence build‑vs‑buy decisions; evaluate vendors and emerging AI tooling.
Technical Skills Required–
- Strong hands‑on expertise with:
- LLMs, RAG, embeddings, vector DBs, AI agents, tool‑calling
- Python, APIs, microservices, cloud (Azure/AWS)
- ML lifecycle, inference optimization, cost controls
- Experience operationalizing AI systems (latency, scale, monitoring, rollback).
- Familiarity with AI observability, evaluation frameworks, and guardrails.
Success Metrics
- AI systems move beyond demos into stable, revenue‑ or risk‑impacting platforms.
- Clear reduction in manual effort, fraud risk, or decision latency.
- Teams independently deliver AI solutions using standards defined by this role.
