smriti SMRITI

AI and Data Science professional

Profile Score

About

AI and Data Science professional with hands-on experience building machine learning, computer vision, and LLM-based systems across industry, internships, and academic research. Strong expertise in Python, Docker, and end-to-end AI pipelines, with a focus on deploying practical, scalable solutions for real-world applications.

Skills & Expertise (16)

AI Advanced

8.8/10

Years Exp

GenAI Advanced

8.5/10

Years Exp

Python Advanced

8.5/10

Years Exp

LLMs Advanced

8.3/10

Years Exp

Prompt Engineering Advanced

8.0/10

Years Exp

Fine-tuning SQL Supervised & Unsupervised Learning Model Evaluation CNNs Neural Networks OpenCV Image Classification Docker Git LINUX

Work Experience

Data Scientist (Contract, Remote – USA Client)

PNS Group

Jan 2024 - Jun 2024

Designed, trained, and evaluated machine learning and deep learning models for business-driven use cases. Built LLM-based systems using prompt engineering and iterative experimentation workflows. Developed end-to-end data pipelines in Python covering ingestion, preprocessing, and model training. Deployed and validated ML models in Docker-based environments for internal business applications.

AI / Data Science Intern

Emerging Trends

Jan 2021 - Jan 2021

Built machine learning models on real-world datasets using Python. Implemented complete ML workflows including preprocessing, training, evaluation, and validation.

Research Intern

CEPTA

Jan 2020 - Jan 2020

Worked on applied research involving signal processing and data analysis techniques. Designed experiments, analyzed results, and contributed to technical documentation.

Data Science Intern

UPPCL

Jan 2019 - Jan 2019

Analyzed large operational datasets from power systems to identify usage trends and inefficiencies. Developed predictive models to support demand forecasting and reporting.

Software Engineer

HARMAN International

Apr 2022 - Dec 2023

Built and maintained Python-based data processing pipelines supporting analytics and ML workflows used in production environments for internal analytics. Developed and optimized machine learning and computer vision pipelines deployed in production environments. Containerized applications using Docker to ensure reproducible development and deployment. Collaborated with engineers and product teams in Agile environments to deliver reliable solutions.