Naman Chawla

AI Researcher

Delhi, India1 yr 6 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in building scalable AI systems.
Proven track record in NLP and deep learning research.
Achieved significant accuracy improvements in ML models.

Stackforce AI infers this person is a skilled AI/ML Engineer with a focus on NLP and deep learning.

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Skills

Core Skills

Machine LearningAi SystemsNatural Language Processing

Other Skills

Analytical ModelsFeature SelectionBayesian OptimizationHyperparameter TuningDomain-Specific ModelingTensorFlowBILSTMDeep LearningPython (Programming Language)Neural Language ModelsMatplotlibData StructuresDimensionality ReductionRecommender SystemsBERT (Language Model)

About

AI/ML Engineer specializing in building and deploying scalable, end-to-end AI systems. Experienced across the full ML stack , designing robust backends, managing cloud-native deployments, and architecting agentic RAG pipelines. Passionate about creating intelligent, production-ready applications that deliver real-world impact.

Experience

Coding blocks

2 roles

AI/ML Engineer

Aug 2025 – Present · 7 mos · New Delhi, Delhi, India · Hybrid

Machine LearningAI Systems

AI Intern

Jan 2025 – Jul 2025 · 6 mos · New Delhi, Delhi, India · Hybrid

Delhi technological university (formerly dce)

2 roles

ML Research Intern

Jun 2024 – Mar 2025 · 9 mos · New Delhi, Delhi, India · Hybrid

Extended model to handle multimodal data, integrating datasets of 15,000 and 9,000 headlines.
Developed and compared deep learning NLP models, conducted ablation study.
Used Recursive Feature Elimination for feature selection, dimensionality reduction.
Enhanced performance with Bayesian Optimization and cyclic learning rate, achieving a boost in training accuracy from 95% to 99% and validation accuracy from 93% to 98%.
Performed perturbation analysis, used SHAP visualizations for model robustness.
Drafted a detailed research paper on study methodologies and findings.

Analytical ModelsFeature SelectionMachine LearningNatural Language Processing

ML Research Intern

Jul 2023 – Jun 2024 · 11 mos · New Delhi, Delhi, India · Hybrid

Developed a deep learning NLP model based on a parallel processing configuration of BERT, Multihead Attention, and CNN-BiLSTM.
Engineered domain-specific features with normalization, dimensionality reduction using t-SNE.
Performed hyperparameter tuning using Grid Search to optimize batch size, learning rate.
Achieved 95% training and 93% validation accuracy on 32,000 headlines.

Hyperparameter TuningDomain-Specific ModelingMachine LearningNatural Language Processing