Senior Principal Engineer

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.

Required Experience

• 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

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.
• Evaluate vendors and emerging AI tooling.

Technical Skills

• LLMs, RAG, embeddings, vector DBs, AI agents, tool‑calling
• Python, APIs, microservices, cloud (Azure/AWS)
• ML lifecycle, inference optimization, cost controls

Operational Experience

• Experience operationalizing AI systems (latency, scale, monitoring, rollback).
• Familiarity with AI observability, evaluation frameworks, and guardrails.

Desired Impact

• 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.