ARYAN SHUKLA

AI Engineer

https://talent.gravityer.com/aryan-shukla-434345

AI Engineer with 1+ years in AI, Machine Learning & BFSI domain.

RBL Bank

Key Strengths

Strong practical experience in AI/ML and NLP, particularly with LLMs (Gemma) in real-world banking scenarios.
Demonstrated ability to build and deploy AI automation tools, including OCR, document parsing, and speech-to-text.
Proficient in Python, SQL, and various data manipulation/analysis libraries (Pandas, NumPy, Scikit-learn).
Experience with data cleaning, feature engineering, and model evaluation.
Good understanding of MLOps concepts through the use of Apache Airflow for DAG deployment.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

About

B.Tech (AI & ML) graduate and Business Analyst at RBL Bank with hands-on experience processing 23 lakh+ rows of banking data, engineering NLP training pipelines, and building production-ready AI automation tools. Sole builder of a Treasury automation system (AI + NLP) awaiting deployment; delivered a Finacle Chatbot training dataset cleaned from 6,300+ to 2,312 rows now in active use for AI response generation. Bridges technical depth and business impact, a rare combination in BFSI.

Top Skills

PythonSQL

Education

Shree Guru Gobind Singh Tricentenary University, Gurugram

B.Tech · Computer Science & Engineering (AI & ML)

August 1, 2020 – June 30, 2024

Experience

RBL Bank

Business Analyst (Intern)- Client Insights

December 1, 2025 – Present

India

Mozaiq Automation Pvt. Ltd.

Data Science Intern

December 1, 2023 – May 1, 2024

India

Projects

Twitter Sentiment Analysis

June 1, 2026 – Present

Developed an NLP-based sentiment pipeline for large-scale Twitter data; built Power BI dashboards to visualize audience sentiment trends and support analytical reporting.

Boston House Price Prediction

June 1, 2026 – Present

Built an ML regression model with feature engineering to predict housing prices; derived actionable pricing insights from model performance evaluation.

Certifications

Probability Modelling using Python

Unknown

June 1, 2026 – Present

Data Analysis using Python

Unknown

June 1, 2026 – Present

AI Foundation

Unknown

June 1, 2026 – Present

Python Programming

Unknown

June 1, 2026 – Present

Key Strengths

Strong practical experience in AI/ML and NLP, particularly with LLMs (Gemma) in real-world banking scenarios.
Demonstrated ability to build and deploy AI automation tools, including OCR, document parsing, and speech-to-text.
Proficient in Python, SQL, and various data manipulation/analysis libraries (Pandas, NumPy, Scikit-learn).
Experience with data cleaning, feature engineering, and model evaluation.
Good understanding of MLOps concepts through the use of Apache Airflow for DAG deployment.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's experience in both a large financial institution (RBL Bank) and a smaller automation company (Mozaiq Automation) demonstrates adaptability to different organizational cultures. Their focus on AI/ML and NLP aligns well with an AI Engineer role. The diversity of projects, from sentiment analysis to house price prediction and complex banking automation, indicates a broad interest in applying AI across various domains. The detailed descriptions of their contributions suggest a results-oriented and collaborative mindset, which are positive indicators for cultural fit.

Soft Skills & Operational Fit

The candidate demonstrates strong problem-solving skills, evidenced by pivoting to a GPT-based extraction pipeline when rule-based tools failed. Their work on automating eligibility checks and forex trade call recordings shows a proactive approach to process improvement and efficiency. The implementation of a human-in-the-loop validation layer indicates an understanding of the importance of accuracy and compliance in critical financial workflows. The detailed descriptions of project contributions suggest good communication of technical work and its business impact.