Kabir Juneja

AI Engineer

https://talent.gravityer.com/kabir-juneja-8258177

AI Engineer with 2+ years in NLP and Machine Learning

ScoreMe Solutions Pvt Ltd

Key Strengths

Extensive experience in production-grade NLP and real-world inference systems, including Transformer+CRF models and RAG pipelines.
Demonstrated ability to optimize AI/ML models for inference using ONNX, TensorRT, and deploy on platforms like NVIDIA Triton Inference Server.
Proficient in cloud platforms (AWS, GCP) and serverless architectures for deploying AI solutions.
Strong background in data processing, feature engineering, and model training for large datasets (100M+ transactions, 176M applications).
Experience with various ML/AI tools and frameworks including PyTorch, TensorFlow, LLMs, YOLOv8, Detectron2, and Scikit-Learn.
Proven track record of achieving significant performance improvements (e.g., 97% F1, 98.84% entity extraction accuracy, 40% latency reduction) and cost savings (90% cost reduction, 60% product profitability increase).
Participation in Google Gen AI Exchange Hackathon (2nd Runner-Up) indicates strong practical GenAI skills.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project diversity, ranging from governance intelligence to financial transaction processing and computer vision, indicates a broad interest and adaptability to different problem domains. Their involvement in a hackathon and continuous learning through certifications (Machine Learning Specialization, Generative AI with LLMs) suggest a proactive and growth-oriented mindset, which aligns well with innovative and fast-paced environments. The detailed descriptions of impact and ownership in projects demonstrate a strong sense of responsibility and initiative. The candidate's experience with both AWS and GCP, along with various open-source and proprietary tools, shows a versatile technical background.

Soft Skills & Operational Fit

The candidate demonstrates strong problem-solving skills through optimizing complex AI pipelines and reducing operational costs. Their experience in owning data creation, training, and inference, along with automating annotation verification, suggests a proactive and end-to-end operational mindset. The detailed descriptions of project impact (e.g., cost savings, accuracy improvements) indicate a results-oriented approach. The candidate's ability to work with diverse technologies and integrate different systems (e.g., Python pipeline with Java fallback) points to adaptability and practical engineering judgment.

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Projects

AI-Powered Governance Intelligence Platform

November 1, 2025 – Present

• Built predictive governance AI for Maharashtra State processing 176M annual applications; trained Gradient Boosting model on Vertex AI (85%+ AUC) for SLA breach prediction with dynamic priority scoring, estimated ₹338.8 Cr annual breach cost. • Deployed production dashboard on Google Cloud Run with BigQuery (176M tickets, <500ms queries), Firestore, and Gemini 2.0 for context-aware insights; architected serverless three-layer system with complete audit trails for State/Ministry/Ground operations, simulating ₹101.2 Cr potential annual savings under given assumptions.

Key Strengths

Extensive experience in production-grade NLP and real-world inference systems, including Transformer+CRF models and RAG pipelines.
Demonstrated ability to optimize AI/ML models for inference using ONNX, TensorRT, and deploy on platforms like NVIDIA Triton Inference Server.
Proficient in cloud platforms (AWS, GCP) and serverless architectures for deploying AI solutions.
Strong background in data processing, feature engineering, and model training for large datasets (100M+ transactions, 176M applications).
Experience with various ML/AI tools and frameworks including PyTorch, TensorFlow, LLMs, YOLOv8, Detectron2, and Scikit-Learn.
Proven track record of achieving significant performance improvements (e.g., 97% F1, 98.84% entity extraction accuracy, 40% latency reduction) and cost savings (90% cost reduction, 60% product profitability increase).
Participation in Google Gen AI Exchange Hackathon (2nd Runner-Up) indicates strong practical GenAI skills.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Kabir Juneja

Key Strengths

Cultural & Operational Fit

About

Top Skills

Skills

Education

Experience

Projects

Certifications

Key Strengths

Cultural & Operational Fit