An HR technology company partnered with Toptal to modernize its AI platform by building a production-grade LLM infrastructure capable of processing millions of data points while improving inference accuracy and data privacy.
The client’s existing large language models (LLMs) produced inconsistent inference quality, limiting the reliability of workforce analytics and employee engagement insights. At the same time, the company needed an AI platform capable of processing millions of records with low latency while maintaining strict data privacy requirements and supporting production-scale deployment of custom language models.
Fine-tuned multiple large language models, including 780M- and 3B-parameter Flan-T5 variants and OpenAI GPT models, using custom, domain-specific training datasets created through expert human annotation. The models achieved more than 98% accuracy on holdout validation datasets before production deployment, outperforming out-of-the-box foundation models.
Designed an end-to-end MLOps pipeline using AWS Step Functions, Amazon SageMaker, AWS Lambda, Amazon S3, and multiple GPU instances to orchestrate model training, parallel inference, deployment, and automated workflow execution at enterprise scale.
Engineered a scalable information retrieval platform capable of processing million-row datasets using GPU-generated embeddings. Toptal evaluated retrieval-augmented generation (RAG), Amazon Bedrock foundation models, and AWS Inferentia accelerators to optimize large-scale inference performance.
Refactored GPU-dependent XGBoost and SHAP workloads into a CPU-optimized architecture using Amazon Elastic File System, AWS Lambda, and massively parallel execution, increasing pipeline throughput by more than 20x while reducing average runtime from 10 minutes to approximately 30 seconds. The complete platform was deployed using infrastructure as code (TypeScript and AWS Cloud Development Kit), enabling rapid, repeatable production deployments.
The engagement delivered a production-ready AI platform capable of processing millions of data points while achieving more than 98% inference accuracy on validation datasets. By combining custom LLM fine-tuning, distributed GPU inference, optimized retrieval pipelines, and AWS-native MLOps, Toptal enabled the client to deploy highly accurate, privacy-conscious language models that outperformed standard foundation models while dramatically improving scalability, deployment speed, and operational efficiency.
