Open to AI/ML, Software Engineering, and Data roles

AI/ML Engineer | Software Engineer

Building practical AI/ML models and reliable software systems.

Computer Engineering graduate with experience in AI/ML, Python development, and MLOps through internships and personal projects. Currently working as a Software Engineer Trainee and interested in building practical machine learning applications and software solutions.

View Projects Resume

PythonScikit-LearnFastAPIDockerGitHub ActionsStreamlitMLflow

Ahmedabad, Gujarat, India

Nidhi TankOpen to Roles

Candidate Overview

Focus: AI/ML · Backend APIs · MLOps

Core Stack: Python, Scikit-Learn, FastAPI, Docker

Experience: Software Engineer Trainee at Impero IT Services

AI/ML ENGINEERMLOpsGENERATIVE AIRAG & LANGCHAINFASTAPISTREAMLITDOCKERCI/CDPOWER BIPYTHONMONGODBAI/ML ENGINEERMLOpsGENERATIVE AIRAG & LANGCHAINFASTAPISTREAMLITDOCKERCI/CDPOWER BIPYTHONMONGODB

01About

Computer Engineering graduate focused on AI/ML

Introduction

I'm a Computer Engineering graduate from L.D. College of Engineering with a CGPA of 8.71. During my degree I got interested in AI/ML and spent my last two years building projects and picking up practical skills through coursework and internships.

I enjoy working on problems that involve data, automation, and building applications that turn models into useful tools.

I completed a 6-month on-site internship at Impero IT Services where I worked on ML features and backend development. Before that, a 2-week summer programme at Infolabz introduced me to data analytics and Power BI in a work setting.

I'm currently continuing at Impero as a Software Engineer Trainee. I'm actively looking for roles in AI/ML, software engineering, or data engineering where I can contribute and keep growing.

Build

Work on supervised ML tasks — data prep, training with Scikit-Learn, and measuring model performance with standard metrics.

Track

Log experiments with MLflow and version datasets with DVC so results are reproducible and easy to compare.

Deploy

Serve models via FastAPI, containerise with Docker, and automate testing pipelines with GitHub Actions.

Analyse

Use Pandas and Matplotlib to explore data, and Power BI to build dashboards — practised during my analytics internship.

02Skills

Skills mapped to the ML lifecycle

AI & Machine Learning

Worked on classification, regression, and NLP tasks in personal projects. Explored Generative AI and RAG through a dedicated Udemy course.

Machine LearningDeep LearningGenerative AIRAGLangChainScikit-LearnModel Evaluation

Core discipline

Programming Languages

Python is my primary language. C and Java covered in academic coursework.

PythonCJava

MLOps & Infrastructure

Used Git throughout my work. Applied Docker, GitHub Actions, and AWS S3 in my insurance prediction MLOps project.

GitGitHub ActionsAWS (S3)DockerCI/CD Pipelines

APIs & Web Apps

Built a FastAPI prediction endpoint for my MLOps project. Used Streamlit for interactive UIs and Supabase for backend auth in a college prototype.

FastAPIStreamlitSupabaseREST APIs

Data & Analytics

Use Pandas and SQL regularly for data work. Built Power BI dashboards during my analytics internship at Infolabz.

MongoDBSQLPandasPower BIMatplotlib

03Experience

Applied AI work with production habits

Software Engineer Trainee

Impero IT Services Pvt. Ltd.

Jul 2026 – PresentFull-time Employee

Ahmedabad, Gujarat, India · On-site

Retained as a full-time Software Engineer Trainee after completing my 6-month internship at the same company.
Contributing to development tasks, participating in team sprints, and applying what I built during the internship in a live environment.
Working alongside senior engineers — learning from code reviews, design discussions, and day-to-day engineering decisions.

Software Engineer Trainee

Impero IT Services Pvt. Ltd.

Jan 2026 – Jun 2026Internship

Ahmedabad, Gujarat, India · On-site

6-month on-site internship working on ML features and backend development within a software engineering team.
Used Python, FastAPI, and Docker to build model-serving endpoints. Set up a basic GitHub Actions CI pipeline for automated testing.
Attended daily standups, worked through sprint backlogs, and had code reviewed by senior engineers — first experience with a structured engineering workflow.

Data Analytics & ML Intern

Infolabz IT Services PVT. LTD

July 2025 · 2 WeeksInternship

Online

2-week online summer programme covering data analytics, Power BI, and applied machine learning.
Retrieved and processed data from APIs using Python (Pandas, Matplotlib) and visualised trends in Power BI.
Trained and evaluated simple regression and classification models on provided datasets as guided exercises.

04Projects

Case studies recruiters can scan

Featured Project

Vehicle Insurance Cross-Sell Prediction

Featured Project

Vehicle Insurance Cross-Sell Prediction

Problem

Predict which existing health insurance customers are likely to purchase vehicle insurance — a standard classification problem applied in insurance analytics.

Built

Built a structured ML pipeline in Python with data ingestion from MongoDB, preprocessing, model training (classification), experiment tracking via MLflow, and a FastAPI inference endpoint. Added GitHub Actions for CI and Docker for containerised deployment.

Takeaway

This project covers the full pipeline from raw data to a served model — a good way to learn how each stage connects in a real workflow.

Pipeline

01Ingest02Validate03Train04Track05Deploy06Monitor07Retrain

PythonScikit-LearnMLflowDVCAWS S3DockerGitHub ActionsFastAPIMongoDB

View Repository

Movie Recommendation System

Problem

Given a movie title, suggest similar titles from the dataset based on content features like genre, keywords, and cast.

Built

Used Python and Pandas to preprocess movie metadata from a public dataset. Applied Count Vectorization on text features and used Cosine Similarity to rank and return the top-N similar movies for any given input.

Takeaway

A practical introduction to content-based filtering, text vectorization, and similarity search — useful grounding for NLP and recommendation work.

PythonPandasScikit-LearnNumPy

View Repository

Class Notification & Timetable App

Problem

Students and faculty at our college had no simple way to communicate class cancellations or timetable changes in real time.

Built

Built a Streamlit app with Supabase as the backend database and auth layer. Added role-based views for students and faculty, real-time absence reporting, and a personalised timetable display. Developed as a prototype for internal use.

Takeaway

Good experience working with a hosted database, managing user roles, and building a functional multi-user app from scratch.

PythonStreamlitSupabaseSQL

View Repository

05Certifications

Courses & credentials

July 2026

Agentic AI Certified Foundations Associate

Oracle Certified

Aug 2025

AI Foundations Associate

Oracle Certified

Sept 2025

Postman API Fundamentals Student Expert

Postman

Sept 2025

Introduction to Cloud

IBM

July 2025

Machine Learning Terminology and Process

AWS Training & Certification

06Education

Academic foundation

L.D. College of Engineering (GTU)

2021 – 2026

Bachelor of Engineering — Computer Engineering

Grade: 9.00 CPI & 8.71 CGPA

Ananya Vidyalaya (GSEB)

2020 – 2021

HSC — Science Stream

Grade: 90.15% · GUJCET: 98.44 PR

Ananya Vidyalaya (GSEB)

2018 – 2019

SSC

Grade: 88.50%

07Contact

Get in touch

Open to

Entry-level and junior roles in AI/ML engineering, data engineering, and software engineering. Happy to discuss graduate programmes or full-time positions.

Emailntank2024@gmail.com

LinkedInin/nidhitank

GitHubgithub.com/nidhitank

Download Resume