Mayank Aggarwal Email & Phone Number

Delhi, India

Bengaluru, Karnataka, India

I make content in the field of AI and ML and try to explain complex concept in an intuitive manner. Checkout my Youtube Channel at https://www.youtube.com/@tech.mayankagg

As a Machine Learning Engineer and Educator at iNeuron.ai, I contributed to the development of cutting-edge LMS features while teaching and mentoring students in Data Science, Big Data, DSA, and Machine Learning. My role combined hands-on engineering with teaching, empowering students to excel in technical fields and achieve career success.Key Contributions and Achievements:1. Assessment Automation for LMS:- Designed and implemented an automated system for generating quizzes and assignments dynamically based on concepts taught in live classes.- Increased student engagement and improved the overall quality of assessments, resulting in a 30% rise in quiz/assignment attempts.- Leveraged technologies like Python, Flask, and OpenAI APIs to develop and deploy this feature at scale.2. Course Instructor for AI and Data Science:- Delivered live sessions as part of the ML Bootcamp and Full Stack Data Science with GenAI programs, teaching foundational and advanced concepts in:i) Python Programmingii) Data Science and Machine Learningiii) Big Data Technologiesiv)Data Structures and Algorithms (DSA)v) Deep Learning & GenAITaught over 1500+ students, maintaining an average rating of 4.8/5 for engaging and impactful sessions.Educational Content Creation:Developed and presented interactive course material tailored to beginner and advanced learners in fields like Machine Learning, NLP, and GenAI.Focused on real-world applications and problem-solving approaches to equip students with industry-ready skills.Key Technologies and Skills:AI and ML Tools: Python, Flask, LangChain, OpenAI APIs, scikit-learn, nltk.Data Science and Big Data: Data Preprocessing, Feature Engineering, SQL, Hadoop.Software Development: Flask-based LMS feature development, automation systems.

Remote

Description:As a mentor and career coach at Scaler, I’ve guided 300+ students on their journey to success in Data Engineering, Big Data, Data Structures & Algorithms (DSA), System Design, and Software Development. My sessions are highly rated for their clarity, actionable insights, and personalized attention to mentees’ career goals.Key Achievements:- Elite Scaler Mentor Award: Recognized as a Top 50 Mentor with an average mentee rating of 5/5, reflecting a consistent commitment to quality and impact.- Successfully helped hundreds of mentees secure positions at top-tier tech companies, including FAANG and other industry leaders.- Praised for a friendly, approachable attitude and the ability to simplify complex technical concepts, making them accessible to learners of all levels.

India

Worked as a Senior Software Developer and AI Engineer in the Learning Apps Team, handling Mindtickle’s core product and driving the integration of AI-powered features for enhanced customer adoption. Leveraged expertise in Large Language Models (LLMs), LangChain, and backend systems to deliver impactful AI-driven solutions while maintaining robust system architecture using Golang, GraphQL, and AWS Lambda.Key Contributions and Projects:1. Assess AI (AI-Powered Assessment Automation):- Automated the creation of assessments by integrating OpenAI’s GPT APIs to generate questions from internet content or pre-existing company modules (e.g., courses and updates).- Reduced assessment creation time from hours to 5 minutes, increasing adoption rates by 20% across customers.- Handled the Backend for Mindtickle Copilot to provide a chat level interface which companies can use to create assessment. Leveraged apache kafka and Cassandra DB for providing a real time experience.2. Topic-Based Grouping for Assessments:- Led the development of a feature to group assessment questions by topic, enabling better evaluation and analytics at the topic level.- Enhanced randomization logic for questions within topics, improving fairness and analytical insights.Feature adopted by 30% of customers, including major enterprises like CISCO and Henkel.

Bangalore Urban, Karnataka, India

Worked as a Technical Associate in the Controllers Technology division, building scalable platforms to support the firm’s regulatory reporting and financial operations globally. Leveraged cutting-edge Big Data technologies, Java, and internal proprietary systems.Key Achievements:Accelerated Promotion: Promoted from Technical Analyst to Technical Associate in recognition of exceptional performance and leadership in high-impact projects.Global Collaboration: Coordinated with teams across London and New York, ensuring seamless delivery of critical financial applications.1. Data Migration for Repo SourcingMigrated legacy data pipelines for sourcing Repo data to a modern data lake architecture, reducing processing delays by ~4 hours daily.Validated and enriched JSON-based contract data using custom refiners for seamless integration with downstream systems.2. Intra-US Transfer Pricing AutomationAutomated the division of $2B worth of expenses among US entities, replacing legacy Excel macros with a modern JavaRDD-based solution.Built data pipelines to source and enrich revenue, expense, and headcount data, improving process transparency and reducing manual effort by 40+ hours/month.Skills and Technologies:Big Data: Apache Spark, Data Skew Optimization, Data Partitioning.Programming: Java (RDD-based development), SQL, Python.Data Engineering: Building and optimizing pipelines, working with large-scale datasets (~900 GB/year).Tools and Frameworks: Sybase ASE, Mithra ORM, proprietary GS technologies.

Contributed as a Big Data and Software Development Engineer in the Controllers Technology division, supporting critical regulatory and operational workflows. Worked extensively with Apache Spark, Java, and proprietary tools to optimize data pipelines, enhance reporting capabilities, and deliver scalable financial solutions.Key Projects and Contributions:1. Repo Fails AutomationAutomated the reversal of failed repurchase agreements in the General Ledger, saving 60+ hours per month for controllers.Developed a backend solution using Java RDD for high-speed calculations on enriched data stored in a SQL data lake.Ensured scalability and accuracy for ~30,000 daily transactions across multiple entities.2. Trade Count Refiner (Big Data Focus)Built a Spark-based pipeline to automate trade count calculations for regulatory filings, reducing processing time from 15 days to 5 hours for annual reports.Tackled data volume challenges (~900 GB/year) by addressing data skewness using advanced techniques like salted joins and partition optimization.Enabled seamless aggregation across ~150M records, ensuring accuracy and compliance with regulatory standards.

Gurgaon, India

Worked as a Machine Learning Engineer in the Conversion Department at OYO, focusing on solving business challenges in the hospitality domain using Data Science, MLOps, and Big Data tools.Key Contributions:1. Image Scoring & Enhancement:- Developed a machine learning model to automatically score and enhance images, eliminating the need for manual intervention.- Deployed the model using Docker and AWS SageMaker, ensuring production readiness for multiple categories such as hotels, homes, and townhouses.2. Learning to Rank:- Designed and implemented a personalized ranking model for hotel listings using XGBoost and Deep Learning algorithms.- Integrated the model with MLOps pipelines to make the ranking system adaptive and scalable.3. Big Data Analytics for Business Optimization:- Used Google BigQuery and AWS tools for processing large datasets to recenter locality clusters based on URN (Usable Room Nights).- Created dynamic dashboards to report key metrics like GMV, NRV, views, and bookings at granular levels.Key Skills Used:MLOps (AWS SageMaker, Docker), Deep Learning, Big Data Analytics (Google BigQuery), Data Science, and Dashboarding.Achievement:Delivered production-ready solutions that significantly improved OYO’s user experience and conversion rates across platforms.

Gurgaon, India

Interned under the Conversion Department at OYO’s corporate office, Gurgaon, focusing on enhancing user flow and optimizing booking conversions across OYO’s platforms.Worked on two core projects that combined machine learning, data science, and big data technologies:1. WhatsApp Automation Tool:- Developed a machine learning-based chatbot system to automate customer communication on WhatsApp.- Integrated with AWS services like Lambda and EC2 for deployment and scalability.- Leveraged Google BigQuery for querying large datasets to extract insights and train the model.2. Delhi Convergence 2X Tool:- Designed and implemented a tool to identify patterns in user behavior and enhance convergence rates for Delhi’s user base.- Used AWS S3 and Google BigQuery for data handling and ML models for behavior prediction.Key Skills Gained:Machine Learning, AWS (Lambda, EC2, S3), Google BigQuery, Data Science, and Big Data Technologies.Achievement:Received a Pre-Placement Offer (PPO) based on exceptional performance and technical contributions.

New Delhi Area, India

Acting as Vice President of IEEE NSIT Student Branch chapters with the responsibility to manage all technical activities of the society while leading a team of more than 30 executive committee members.• Delhi Section Congress (Oct 2017)Prepared content, teaching material & delivering weekly session to Fresher’s on Introduction to Data Science & Machine Learning.mentored students to apply these concepts to make useful industry standard projects.Started a blog on medium where notes were shared with students on weekly basis.• Java Bootcamp (Oct 2017)Workshops conducted in partnership with Coding Ninjas, with over 150 participants.

New Delhi Area, India

Worked under Dr Tanmoy Chakroborty on enlisted research projects :--> Worked on to build an algorithm which combines clustering and classification for ensemble learning.o My role was to Code & implement the algorithm wherein result from various algorithms were combined in form of Matrices & improved Classification results were obtained by Ensemble Model.--> URL Phishing Detection using Frequent Substring Patterns :o My role was to work on to finding frequent substring patterns to prepare an edge list of common patterns in Links.o Further, graphical inference was carried out with an aim to build a system for url phishing detection.