Building scalable, reliable, and cost-aware Generative AI systems at production scale
I’m a Staff Engineer with 9.5+ years of experience designing and leading cloud-native AI/ML platforms, with a strong focus on LLMs, agentic systems, and observability.
My work centers on end-to-end LLM infrastructure — from gateways and agent frameworks to production-grade observability — enabling teams to ship reliable, governed, and cost-efficient Generative AI solutions with real business impact.
I enjoy solving hard platform problems where scale, reliability, cost, and developer experience intersect.
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LLM Platform Architecture
Gateways, routing, model abstraction layers, and LLM Ops -
Agentic Systems
Multi-agent orchestration, tool execution, and stateful workflows -
LLM Observability & Governance
Cost tracking, evaluation, tracing, and policy enforcement -
Cloud-Native ML Systems
Scalable deployments on AWS with Kubernetes-first design -
Production AI Enablement
Helping teams ship GenAI safely, predictably, and confidently
- LLM gateways & inference platforms
- Agent frameworks: LangGraph, SmolAgents
- AWS Bedrock: Agents & Strands
- Prompt engineering, evaluation, and cost optimization
- Langfuse, Arize Phoenix
- OpenTelemetry for traces, metrics, and logs
- Token usage, latency, quality, and governance tracking
- AWS, Databricks
- Kubernetes & Docker
- CI/CD for ML systems
- Secure, multi-tenant platform design
- Python (primary)
- Rust (systems & performance)
- Data engineering & large-scale NLP systems
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📝 I regularly write about AI platforms, LLM systems, and engineering best practices
👉 https://varunk.me/ -
💬 Happy to discuss: LLMs, Agentic AI, LLM Ops, MLOps, Kubernetes, Platform Architecture
- 🌐 Website: https://varunk.me/
- 💼 LinkedIn: https://linkedin.com/in/saivarunk
Building production-grade AI systems that balance innovation, reliability, and real-world value.