Kunal Kumar

Machine Learning Engineer

Austin, Texas, United States4 yrs 10 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in machine learning and natural language processing.
Proven track record in optimizing large language models.
Strong background in software engineering and project management.

Stackforce AI infers this person is a Machine Learning and Software Engineering professional with a focus on NLP and AI technologies.

Contact

kunal.kumar.eee14@itbhu.ac.in LinkedIn

Skills

Core Skills

Machine LearningNatural Language ProcessingSoftware EngineeringProject Management

Other Skills

Algorithm DesignAmazon Web Services (AWS)Artificial IntelligenceArtificial Intelligence (AI)AutomationBlockchainCC++CI/CDCSSCompetitive ProgrammingComputer VisionData AnalysisData ScienceData Structures

About

With 3+ years of industry experience and 2 years of academic research, I am a machine learning researcher and a MS CS student at the University of Massachusetts Amherst. My mission is to develop and apply cutting-edge techniques for natural language processing and model compression, and to solve real-world problems with artificial intelligence. Currently, I am working on deep learning systems for quantizing and optimizing large language models, resulting in significant reductions in model size and increases in inference speed. I have also collaborated with Oracle Labs and AMD on projects involving ground learning and post-training quantization of LLMs, respectively. As a technology enthusiast and a lifelong learner, I am passionate about exploring new technologies, enhancing my skills, and making an impact. Moreover, I am "Open" to full-time roles in ML/AI and SDE domain. My expertise is in LLMs, GenAI, NLP, and CV for ML/AI roles and frontend & full-stack technologies for SDE roles.

Experience

4 yrs 10 mos

Total Experience

2 yrs 11 mos

Average Tenure

1 yr 11 mos

Current Experience

Amd

2 roles

Machine Learning Engineer

Jul 2024 – Present · 1 yr 11 mos · Austin, Texas, United States · On-site

Machine Learning Intern

Jun 2023 – Aug 2023 · 2 mos · San Jose, California, United States · On-site

As a part of Machine Learning Solutions Team in Artificial Intelligence Group at AMD, my role in the internship were :
Designed a dockerized pipeline to quantify the efficiency of Post-Training Quantized (PTQ) NLP models, resulting in a 41% & 75% reduction in model size and a 2x & 1.77x speedup in inference for PyTorch & ONNX models.
Fine-tuned LLMs like BERT & Llama on SQuAD and GLUE dataset achieving 85% accuracy for quantization task.
Exported trained weights from Deep Learning Recommendation Model (DLRM) model to ONNX & optimized the model via quantization using AI compiler.
Transformed the model into ONNX format, performed operator analysis, and executed model inference.
Exposure: Natural Language Processing(NLP), Computer Vision, Deep Learning, Recommender System, Docker, Linux

PythonPost-Training QuantizationNLPDockerLinuxMachine Learning+1

Oracle

Graduate Student Researcher

Feb 2023 – May 2023 · 3 mos · United States · Remote

Title : Operating Software with Grounded Language Agents.
Industry : Machine Learning Research Group, Oracle Labs
Industry Mentor : Ari Kobren
PhD Mentor : Dhruvesh Patel
Supervisor : Prof. Andrew McCallum
Details :
Developed a Grounded Language Learning model using FLAN-T5 LLM in Pytorch for instruction-based finetuning on
Oracle dataset with a 15% increase in accuracy metrics, including Token F1 and Token Exact Match.
Incorporated various prompting techniques, including Question-Answer, Machine Translation, & Chain-of-Thought to
enhance model evaluation with few-shot learning, improving overall model performance by 20%.
Automated dataset creation using web scrapping tools on Oracle documentation streamlining it by 85%.
Exposure: Python, NLP, LLM, Pytorch, Transformers, TensorFlow, Embeddings, Web Scrapping

PythonNLPLLMPytorchTransformersTensorFlow+3

Jpmorgan chase & co.

2 roles

Associate Software Engineer

Promoted

Feb 2021 – Jul 2022 · 1 yr 5 mos · On-site

Orchestrated successful project deliveries within CI/CD practice, yielding in a 20% enhancement in project delivery efficiency and a 10% surge in production stability collaborating with the Risk & Forecasting Solutions team.
Automated the generation of Value at Risk (VaR) and Stress numbers using Python, improving data calculation accuracy and reducing manual effort by 70%.
Optimized code deployment workflow through parallel processing strategies by 25% & improving overall performance.

PythonCI/CDAutomationParallel ProcessingSoftware EngineeringProject Management

Software Engineer 1

Jul 2019 – Jan 2021 · 1 yr 6 mos · On-site

Indian institute of information technology

Summer NLP Research Intern

May 2017 – Jul 2017 · 2 mos · Allahabad Area, India

Summer Research Internship in SILP(Signal Processing, Image Processing, and Language Processing) Lab.
Project : Development of a syllable based unit selection method for Hindi text to speech generation. (A part of Dialogue System)
About : This was 2 months of research internship under SILP Lab. The key area to focus was Natural Language Processing, Machine Learning, Database Management and Semantic Web. Overall task was to create a system where if user feed a question than the system will apply all the ML and NLP tools to extract the key words by data cleansing, data enhancement and format it. The formatted data is matched was managed with SparQL and semantic web to produce a result to be outputted and displayed to the user.
Tools : Python3, NLTK, Pandas, Numpy, Semantic Web, SparQL, Etc.