Karamvir Singh — Lead ML Engineer

A quietly ambitious builder who blends precision with imagination. I take complex problems, befriend their ‘zombies,’ and turn them into elegant, scalable systems — all while keeping a mysterious edge ------------- Staff Engineer at HighLevel | Specializing in LLM Systems, AI Agents, and Scalable ML Infrastructure -------------- I'm an applied AI engineer focused on taking research-grade innovations and deploying them at scale in production environments. I work across the full AI lifecycle—from training and fine-tuning models to orchestrating multi-agent systems and optimizing inference pipelines. ------------------ Current Role At HighLevel, I’m leading initiatives to: 1. Build platform-wide LLM agents across content, support, and automation use cases 2. Orchestrate multi-agent systems using retrieval, routing, and tool augmentation techniques 3. Optimize agent grounding, latency, and accuracy through fine-tuning, distillation, and scalable infra Previous Impact 1. At UnifyApps, led the architecture for a multimodal agentic platform integrating voice, text, and vision, deployed across 10+ enterprise clients. Delivered over $1M in cost savings. 2. At Sprinklr, built high-throughput conversational systems combining ASR, TTS, and transformers, achieving 1500 RPS and 50%+ automation in customer interactions. Core Focus Areas 1. Large Language Models (LLMs): RAG, LoRA, Peft, prompt engineering, evaluation 2. Multi-Agent Systems: orchestration, tool use, agent routing, evaluation frameworks 3. Voice AI: speech recognition (Wav2Vec2, Whisper), TTS (VITS), spoken dialogue systems 4. Infrastructure: GPU-sharing, batching, containerized inference, CI/CD, GCP, AWS Additional Highlights 1. Published research on Explainable AI for education decision-making (IJRASET, 2023) 2. Strong foundation in deep learning, classical ML, and NLP

Stackforce AI infers this person is a SaaS-focused AI Engineer with expertise in LLMs and multi-agent systems.

Experience: 5 yrs