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Lead AI Engineer @ HCLTech | Building Agentic Engines | Harness Engineer | Retrieval Engineer
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I'm an AI/ML Engineer with experience in developing conversational AI systems and workflows across verticals.
The George Washington University
Master of Science - MS, Data Science
May 1, 2025 – Present
2021 Qiskit Global Summer School on Quantum Machine Learning
Certificate of Quantum Excellence, Quantum Computing, Quantum Machine Learning
July 1, 2021 – August 1, 2021
Frontier Technology Institute
Introduction to Quantum Computing
October 1, 2020 – May 1, 2021
Vellore Institute of Technology
Bachelor of Technology - BTech, Computer Science Engineering (specialization in Artificial Intelligence and Machine Learning )
N/A – Present
HCLTech
Lead AI Engineer
August 1, 2025 – Present
United States
Temporai
AI Engineer II
June 1, 2025 – August 1, 2025
The George Washington University- Milken Institute School of Public Health
Generative AI Developer
January 1, 2025 – December 1, 2025
United States
Temporai
AI Engineer
August 1, 2024 – June 1, 2025
Temporai
Data Scientist
May 1, 2024 – August 1, 2024
The George Washington University
Student Research Specialist III
March 1, 2024 – July 1, 2024
Washington, District of Columbia, United States
The George Washington University
Graduate Assistant
January 1, 2024 – May 1, 2025
Washington, District of Columbia, United States
Appronic Software Pvt. Ltd.
Data Scientist
April 1, 2022 – March 1, 2023
QWorld
Research Assistant
July 1, 2021 – August 1, 2021
Remote
Llama-MD
August 1, 2024 – December 1, 2024
A Llama based chatbot for medical advise. We fine-tuned Llama 3.2 using LoRA on medical Q&A Dataset to improve model performance and give accurate responses for medical advise.
RateMe
November 1, 2023 – December 1, 2023
RateMe is an advanced app rating prediction platform using a Light Gradient Boosting Machine (LGBM) model to forecast Android app ratings with high accuracy. It integrates 14 key features like category, ad support, and app size, targeting developers and aspiring creators to optimize app success before market launch.
QueryQuirks
August 1, 2023 – December 1, 2023
QueryQuirks is a project that focuses on the peculiar aspects or "quirks" of database queries by analyzing the performance of databases like SQL, MongoDB, Neo4J, and Hadoop. It benchmarks these systems using Python, focusing on efficiency metrics such as time, memory, and CPU usage, and offers insights for database optimization.
AirScore
August 1, 2023 – December 1, 2023
The project focuses on analyzing factors affecting airline passenger satisfaction. It explores how passenger characteristics and flight experience components influence satisfaction. The study involves data pre-processing, examining variable distributions, correlation matrices, and constructing models (OLS estimates, logit model, and decision tree) for predicting satisfaction.
Data Streaming Pipeline
May 1, 2023 – Present
Established infrastructure and pipelines for analysing data from streaming services, simulated music streaming data, orchestrated data movement with Apache Spark, Terraform, Apache Airflow, and dbt, enabling data-driven insights on a dashboard.
Climate Change Predictor
April 1, 2022 – Present
Employed SARIMA and ARIMA models with JAX and Google APIs to accurately predict climate changes, reducing turnaround time and presenting visualizations comparing old and new climate data.
Customer Segmentation in R:
November 1, 2021 – Present
Implemented K-Means clustering in R for customer segmentation based on age, income, and spending. Conducted data exploration, employed advanced clustering techniques, and visualized results using PCA.
Twitter Sentiment Analysis
October 1, 2020 – Present
Utilized Python, Recurrent Neural Networks, Keras, and nltk to develop a model with 91% accuracy for sentiment analysis on Twitter, facilitating product improvement and brand image monitoring.
Red Hat System Administration I (RH124)
Red Hat
June 25, 2026 – Present
Introduction to Cloud Identity
June 25, 2026 – Present
Migrating to Google Cloud
June 25, 2026 – Present
Data & AI Essentials
IBM
June 25, 2026 – Present
Introduction to AWS Identity and Access Management
Amazon Web Services (AWS)
June 25, 2026 – Present
Google Cloud Fundamentals for Azure Professionals: Core Infrastructure
June 25, 2026 – Present
HTML, CSS, and Javascript for Web Developers
The Johns Hopkins University
June 25, 2026 – Present
IBM Machine Learning Professional Certificate
IBM
June 25, 2026 – Present
Getting Started with AWS Machine Learning
Amazon Web Services (AWS)
June 25, 2026 – Present
Google Cloud Platform Fundamentals for AWS Professionals
June 25, 2026 – Present
Natural Language Processing with Classification and Vector Spaces
DeepLearning.AI
June 25, 2026 – Present
IBM Quantum Challenge - Fall 2021 - Advanced
IBM
June 25, 2026 – Present
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from app rating prediction to climate change prediction and medical chatbots, demonstrates a broad interest in applying ML/AI across various domains. Their academic background with a specialization in AI/ML and ongoing Master's in Data Science, coupled with multiple certifications, indicates a strong commitment to continuous learning and professional development. The experience in both academic research and industry roles (HCLTech, Temporai, Appronic Software) suggests adaptability to different work environments. The target role of ML Engineer aligns well with their demonstrated skills and career trajectory, particularly in Generative AI and data-driven insights. However, the lack of specific team collaboration or cross-functional project details in the provided experience makes it difficult to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions and experience indicate a proactive approach to problem-solving and a focus on improving efficiency and accuracy. Roles like 'Lead AI Engineer' and 'Student Research Specialist III' suggest leadership potential and the ability to drive projects. The emphasis on reducing hallucinations in LLMs and enhancing model training efficiency points to a detail-oriented and performance-driven mindset. However, without psychometric test results, a comprehensive assessment of work attitude, stress handling, and team collaboration is not possible.