Summary

• Experienced ML Engineer (Python) with 6 years specializing in demand forecasting, machine learning, and deep learning across diverse industries.
• Proficient in advanced data analytics, large dataset handling, and optimizing demand patterns.
• Committed to driving innovation and enhancing business outcomes through data-driven strategies

Technical Skills

• Python Programming: NumPy, Pandas, Sci-py, Scikit-learn, Seaborn, Matplotlib, Flask, Django, OOPS
• Machine Learning: Supervised and Unsupervised ML Algorithms
• Time Series Analysis: AR, MA, ARIMA, SARIMA models for time series data analysis
• Deep Learning
• Natural Language Processing: Text understanding, generation & classification techniques, Text clustering skills
• Libraries: NLTK, HuggingFace, Gensim, TextBlob, Langdetect
• Databases: MySQL, MongoDB, 3C (Command, Constrains, Clauses), CRUD operations, Joins
• Other Tools: Docker, GIT, AWS , GCP
• Language Model (LLM): OpenAI and Open Source Models, Prompt Engineering
• Power Tools: Power Automate Desktop, Power Virtual Agents, Copilot Studio, Power BI, AI Builder

Certifications

• IBM Data Science Professional Course.
• Machine Learning Fundamentals and Feature Engineering.
• Machine Learning by Stanford University.
• Python (Basics)

Professional Experience

Role: ML Engineer
Duration: 04/2021 – Present

 

Intelegencia
Role: Associate ML Engineer
Duration: 08/2018 – 04/2021

Roles and Responsibilities:

• Conducted Python-based data analysis, ML model training, and pipeline development, ensuring streamlined data workflows.
• Deployed ML models, including Large Language Models (LLMs), on IIS servers, optimizing performance and scalability for diverse applications while resolving deployment challenges.
• Developed advanced predictive models using logistic regression, decision trees, neural networks, specialized time series analysis, and LLMs, driving substantial business improvements.
• Conducted comprehensive data visualization and summarization, translating complex insights into actionable recommendations for informed decision-making across departments.
• Led cross-functional collaborations with product, engineering, and marketing teams to innovate and implement data-driven solutions that propel business growth and enhance operational efficiency.

Projects Worked On

Electro-tech Trouble Assistant:

• Developed an AI-powered chatbot to diagnose and resolve software and hardware issues.
• Utilized predefined Troubleshooter documents and NLP for accurate and relevant answers.
• Automated customer offer generation for efficient operational support. Refined the chatbot continuously through user feedback and external tool integration.

Skills: Copilot Studio, Power Tools, Connectors, Dataverse

 

AI-LogiOps Optimizer:

• An innovative logistics project employing LLMs to revolutionize air courier operations.
• The solution enhances customer satisfaction and operational efficiency by dynamically adjusting Machine Learning Model responses based on intricate customer preferences.
• Incorporates intelligent features such as precise travel time calculations, day-wise variations, and real-time alignment with flight carrier preferences.
• The result is a seamless, customer-centric logistics experience captured in a structured JSON format.

Skills: Python, LLMs, Prompt Engineering

 

Logistic Chatbot using Power Vision Studio:

• Developed a Logistic Chatbot using Power Vision Studio, incorporating OpenAI for advanced functionalities.
• Integrated OpenAI for function calling, entity extraction, and processing orders, including order creation, tracking, modification, and cancellation.
• Streamlined logistics operations, enhancing efficiency and customer experience.

Skills: Python, Power Automate, Power Vision, OpenAI, Prompt Engineering

 

Agent and Flight Selection using Machine Learning

• Developed an advanced machine learning model for predicting courier service requirements in real-time.
• By analyzing historical delivery data and customer demands, optimized resource allocation, minimized delays, and ensured top-notch customer satisfaction for a leading logistics company.
• Automated the training of the model.

Skills: Python, Machine Learning (Classifiers), Data Visualization, and Report Making

 

Electronic Product Review Sentiment Analysis

• Analyzed the sentiment of electronic product reviews using Natural Language Processing (NLP) techniques.
• The goal was to extract and classify consumer opinions as positive, negative, or neutral, providing insights into consumer attitudes towards client's electronic products.

Skills: NLP, Python, Machine Learning (Classifiers), EDA, Preprocessing