Validate at inference time, not post-processing. Use structured outputs with schema enforcement. Implement multi-layer validation: input sanitization → model guardrails → output verification.

Set temperature=0 for factual queries. Use structured outputs with Pydantic validation. Implement reproducible seeds for consistent results. Schema-first approach reduces variability.

Implement confidence thresholds (e.g., <0.7 → human review). Build fallback chains: primary model → smaller model → rule-based → human escalation. Use exponential backoff for retries.

Log prompts, responses, tokens, latency, costs. Trace requests end-to-end. Monitor hallucination rates, user feedback, error rates. Set up alerts for anomalies. Use structured JSON logs. Track model performance metrics, token usage, and costs in real-time dashboards.

Set max iterations/tokens per agent. Validate each step before proceeding. Use agent orchestrators to coordinate multi-agent workflows. Implement circuit breakers for runaway agents.

Q: What makes you different from other AI engineers? A: Most AI engineers fall into two camps: pure ML researchers who struggle with production systems, or backend engineers learning AI. I'm the rare intersection of both. • 4+ years architecting distributed systems at enterprise scale — TCS: Built backend systems serving 50K+ users with 99.9% uptime for aerospace clients — JLL Technologies: Shipped production GenAI systems reducing analysis time from 4 hours to 30 seconds • 1.5+ years shipping production LLM applications with real users • Track record of reliability: reduced hallucinations by 35% across systems • Proven impact: 100K+ buildings served, 50K+ monthly active users • Hackathon validation: 1st place Azure AI Developer Hackathon 2025 • Continuous learning: Actively participate in AI hackathons and attend cutting-edge AI tech events to stay ahead of the curve I don't just understand transformers and RAG pipelines—I know how to wrap them in guardrails, observability, and failover logic that keeps them running 24/7. I ship production-grade AI that business leaders trust, not fragile demos that break under load.