Machine Learning Engineering

NIMIT DAVE

+1-617-***-**** *************@*****.*** LinkedIn Github / Portfolio

EDUCATION

Northeastern University, Boston May 2024

Master of Science in Data Analytics Engineering GPA: 3.8/4.0 Relevant Coursework: Machine Learning Operations - MLOps, Applied Natural Language Processing, Statistical Learning for Engineering, Foundations of Data Analytics (Data Analysis, Data Science), Data Mining in Engineering Mumbai University, India May 2022

Bachelor of Engineering in Mechanical Engineering GPA: 3.7/4.0 Relevant Coursework: Applied Mathematics, Applied Machine Learning, Artificial Intelligence, Reinforcement Learning, Supervised Learning, Unsupervised Learning, Intro to Computer Science, Computer Engineering, Data Structure and Algorithms WORK EXPERIENCE

Blockhouse.app NYC, USA

Machine Learning Engineer June 2024 – Current

• Developed a deep-learning multi-agent trading system that generates optimal trade schedules for a given order and selects the best execution type to minimize transaction costs

• Designed macro-micro agents using Proximal Policy Optimization (PPO) within a reinforcement learning environment combined with a Transformer Encoder, macro agent determines the optimal trade schedule, while the micro agent selects the ideal execution type

(Market or Limit) to minimize slippage, price impact, transaction costs, and spread costs

• Achieved a 10% reduction in slippage, 5% improvement in price impact, and 12% reduction in transaction costs and spread costs, outperforming traditional strategies such as TWAP and VWAP

• Developed model API endpoints the connect Machine Learning models to different components of workflow, effectively deploying to production.

• Led product development for the Blockhouse portal, creating a comprehensive pipeline for generating reports and visualizations that demonstrated the profitability of the company’s trading strategies to potential users

• Contributed to significant platform growth, increasing subscriptions by 7,500 users and boosting revenue by $150,000 through the successful implementation of data-driven insights and visual reporting Washino Mumbai, India

Machine Learning Engineer May 2020 – December 2021

• Developed customer churn prediction models using TensorFlow and PyTorch, boosting prediction accuracy by 20%

• Automated ETL processes with Apache Airflow and Kubernetes, reducing data processing time by 50%

• Deployed ML models on AWS using MLflow and FastAPI, enabling real-time predictions and enhancing application performance.

• Executed A/B tests to validate model updates, ensuring reliability through statistical methods KJSCE Mumbai, India

Software Developer September 2021 - May 2022

• Developed a faculty portal for automatic summarization over 5000 academic documents using NLP and deep learning

• Implemented Data-Driven OCR for data import, automating digitization and database entry for over 3000 scanned documents, streamlining documentation processes displaying communication skills and decision making

• Created an analysis tool using machine learning for sentiment analysis and topic modeling on 2000 university publications. PROJECTS

AI - Driven Job Application System (LangGraph, RAG, Multi Agent, Claude 3) February 2024 - March 2024

• Developed a robust multi-agent system using LangGraph and LangChain, implementing machine learning and Retrieval augmented Generation (RAG), increasing ATS pass-through rates by 30%

• Utilized machine learning to generate customized cover letters tailored to job descriptions, improving relevance by 25%

• Led Agile project development with LangChain and RAG, reducing development time by 20% and enhancing performance Autonomous Stock Trading Bot Using Reinforcement Learning (RL, DDQN January 2022 – March 2022

• Developed an autonomous trading bot using Double Deep Q-Learning (DDQN), achieving a 10% higher return than S&P 500 over 2 years

• Designed a reward function balancing profit maximization, maintaining a controlled maximum drawdown below 5%

• Conducted market simulations and deployed the bot in a paper trading environment, demonstrating its real-time trading potential TECHNICAL SKILLS

Languages: Python, C/C++, SQL, JavaScript, HTML/CSS. Frameworks: React, Node.js, Flask, TensorFlow, PyTorch, Keras, FastAPI, Snowflake, Full Stack, Elasticsearch, Kibana, Jira Developer Tools: Git, Docker, Kubernates, Cloud Computing (Google Cloud Platform - GCP, Amazon Web Services - AWS), Power BI, Tableau Libraries: NumPy, pandas, mathplotlib,, Scikit-Learn, XGBoost, REST API, Postman, NLTK, Hugging Face Transformers, LangChain, LangGraph

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