ML architect

ML architect at Nextgenpros Inc ML architect at Nextgenpros Inc in Campbell, California Posted in 1 day ago.

Type:

full-time

Job Description:

Title:

Machine Learning Architect Location:

San Jose, CA (Hybrid) Long Term Contract On C2C Basically we need an senior Architect/Lead-level AI/ML resource with strong expertise in Applied NLP, Machine Learning, and Data Science to design and build scalable enterprise-grade query understanding and intelligent routing solutions. The ideal candidate should have hands-on experience in recommendation systems, forecasting, entity extraction, semantic retrieval, and low-latency ML systems, along with exposure to LLM fine-tuning, RAG, and modern AI architectures. This role requires both deep technical capability and architectural leadership to drive current implementation needs as well as future AI/LLM initiatives. Responsibilities Design and implement a query understanding pipeline to extract intent, routing decisions, entities, application mapping, and historical evidence from user queries and conversations. Define and build the training data model and annotation schema for structured outputs (intent, routing, entities, applications, evidence). Lead data collection, synthesis, analysis, and cleaning to develop high-quality datasets for model training and evaluation. Develop and evaluate baseline and advanced non-LLM models for: Intent classification Query routing Entity extraction Application detection Evidence retrieval Design and implement advanced ML/Data Science solutions for enterprise use cases, including: Recommendation systems Forecasting and predictive analytics Behavioral and usage pattern analysis Lead experimentation and implementation of LLM-based solutions, including: Fine-tuning and optimization of foundation models Prompt engineering and retrieval augmentation strategies Evaluation and benchmarking of LLM performance for enterprise workflows Build and maintain train, test, and evaluation pipelines with strong focus on: Accuracy and F1 score Confidence scoring and calibration Latency and throughput Optimize models to meet strict constraints: Sub-second inference latency CPU-only execution Compact model size (