Lead AI Engineer | NLP & Machine Learning
Building production-grade AI systems, from tokenization to LLM-powered products at scale.
A technical lead specializing in the intersection of NLP research and robust engineering. I bridge the gap between theoretical machine learning and production-ready applications, with extensive experience in LLM orchestration, transformer optimization, and large-scale MLOps.
Transformers, BERT, GPT-Like Model, Sequence Models
LLM Orchestration, RAG, Fine-tuning, NLP Pipeline Optimization
LLMOps, Scalable Inference, Vector Databases, AWS/GCP AI Infrastructure
MAU Ecosystem Scale
Operational Cost Reduction
Model Quality Improvement
Data Scientist Lead
Leading AI innovation for a high-growth career platform. Architected the AI interview SaaS, optimizing candidate matching through proprietary LLM pipelines.
Tech Lead & ML Engineer
Headed the Speech AI division, focusing on Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems for enterprise clients.
Inefficient manual screening for thousands of job applications.
Deployed AI Voice Interview Agent for candidate assessment.
70% reduction in initial screening time per candidate.
High latency and cost in third-party speech recognition APIs.
Built end-to-end speech pipeline with self-hosted ASR and neural TTS models, pretrained and fine-tuned on localized Indonesian dialects for low-latency enterprise deployments.
$3,500/mo cost savings with 15% better accuracy.
Fragmented model training and deployment cycles across teams.
Designed custom training pipelines with automated retraining triggers, model versioning, and canary deployments for continuous model improvement.
Deployment frequency increased by 4x with zero downtime.
Traditional keyword search failing for complex user intent.
Semantic vector embeddings using Pinecone and bi-encoder models.
26% improvement in search relevance and user satisfaction.
Open for collaborations on production LLM systems, RAG architectures, and technical leadership roles.