👋 Hello, I'm Krishna Vamsi Dhulipalla

I’m a Machine Learning Engineer with over 3 years of experience designing and deploying intelligent AI systems, integrating backend infrastructure, and building real-time data workflows. I specialize in LLM-powered agents, semantic search, bioinformatics AI models, and cloud-native ML infrastructure.

I earned my M.S. in Computer Science from Virginia Tech in December 2024 with a 3.95/4.0 GPA, focusing on large language models, intelligent agents, and scalable data systems. My work spans the full ML lifecycle—from research and fine-tuning transformer architectures to deploying production-ready applications on AWS and GCP.

I’m passionate about LLM-driven systems, multi-agent orchestration, and domain-adaptive ML, particularly in genomic data analysis and real-time analytics.

🎯 Career Summary

👨‍💻 3+ years of experience in ML systems design, LLM-powered applications, and data engineering
🧬 Proven expertise in transformer fine-tuning (LoRA, soft prompting) for genomic classification
🤖 Skilled in LangChain, LangGraph, AutoGen, and CrewAI for intelligent agent workflows
☁️ Deep knowledge of AWS (S3, Glue, Lambda, SageMaker, ECS, CloudWatch) and GCP (BigQuery, Dataflow, Composer)
⚡ Experienced in real-time data pipelines using Apache Kafka, Spark, Airflow, and dbt
📊 Strong foundation in synthetic data generation, domain adaptation, and cross-domain NER

🔭 Areas of Current Focus

Developing LLM-powered mobile automation agents for UI task execution
Architecting retrieval-augmented generation (RAG) systems with hybrid retrieval and cross-encoder reranking
Fine-tuning DNA foundation models like DNABERT & HyenaDNA for plant genomics
Building real-time analytics pipelines integrating Kafka, Spark, Airflow, and cloud services

🎓 Education

Virginia Tech — M.S. in Computer Science

📍 Blacksburg, VA | Jan 2023 – Dec 2024
GPA: 3.95 / 4.0
Relevant Coursework: Distributed Systems, Machine Learning Optimization, Genomics, LLMs & Transformer Architectures

Anna University — B.Tech in Computer Science and Engineering

📍 Chennai, India | Jun 2018 – May 2022
GPA: 8.24 / 10
Specialization: Real-Time Analytics, Cloud Systems, Software Engineering Principles

🛠️ Technical Skills

Programming: Python, R, SQL, JavaScript, TypeScript, Node.js, FastAPI, MongoDB
ML Frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers
LLM & Agents: LangChain, LangGraph, AutoGen, CrewAI, Prompt Engineering, RAG, LoRA, GANs
ML Techniques: Self-Supervised Learning, Cross-Domain Adaptation, Hyperparameter Optimization, A/B Testing
Data Engineering: Apache Spark, Kafka, dbt, Airflow, ETL Pipelines, Delta Lake, Snowflake
Cloud & Infra: AWS (S3, Glue, Lambda, Redshift, ECS, SageMaker, CloudWatch), GCP (GCS, BigQuery, Dataflow, Composer)
DevOps/MLOps: Docker, Kubernetes, MLflow, CI/CD, Weights & Biases
Visualization: Tableau, Shiny (R), Plotly, Matplotlib
Other Tools: Pandas, NumPy, Git, LangSmith, LangFlow, Linux

💼 Professional Experience

Cloud Systems LLC — ML Research Engineer (Current role)

📍 Remote | Jul 2024 – Present

Designed and optimized SQL-based data retrieval and batch + real-time pipelines
Built automated ETL workflows integrating multiple data sources

Virginia Tech — ML Research Engineer

📍 Blacksburg, VA | Sep 2024 – Jul 2024

Developed DNA sequence classification pipelines using DNABERT & HyenaDNA with LoRA & soft prompting (94%+ accuracy)
Automated preprocessing of 1M+ genomic sequences with Biopython & Airflow, reducing runtime by 40%
Built LangChain-based semantic search for genomics literature
Deployed fine-tuned LLMs using Docker, MLflow, and optionally SageMaker

Virginia Tech — Research Assistant

📍 Blacksburg, VA | Jun 2023 – May 2024

Built genomic ETL pipelines (Airflow + AWS Glue) improving research data availability by 50%
Automated retraining workflows via CI/CD, reducing manual workload by 40%
Benchmarked compute cluster performance to cut runtime costs by 15%

UJR Technologies Pvt Ltd — Data Engineer

📍 Hyderabad, India | Jul 2021 – Dec 2022

Migrated batch ETL to real-time streaming with Kafka & Spark (↓ latency 30%)
Deployed Dockerized microservices to AWS ECS, improving deployment speed by 25%
Optimized Snowflake schemas to improve query performance by 40%

📊 Highlight Projects

LLM-Based Android Agent – Multi-step UI automation with memory, self-reflection, and context recovery (80%+ accuracy)

Real-Time IoT-Based Temperature Forecasting

Kafka-based pipeline for 10K+ sensor readings with LLaMA 2-based time series model (91% accuracy)
Airflow + Looker dashboards (↓ manual reporting by 30%)
S3 lifecycle policies saved 40% storage cost with versioned backups
🔗 GitHub

Proxy TuNER: Cross-Domain NER

Developed a proxy tuning method for domain-agnostic BERT
15% generalization gain using gradient reversal + feature alignment
70% cost reduction via logit-level ensembling
🔗 GitHub

IntelliMeet: AI-Powered Conferencing

Federated learning, end-to-end encrypted platform
Live attention detection using RetinaFace (<200ms latency)
Summarization with Transformer-based speech-to-text
🔗 GitHub

Automated Drone Image Analysis

Real-time crop disease detection using drone imagery
Used OpenCV, RAG, and GANs for synthetic data generation
Improved detection accuracy by 15% and reduced processing latency by 70%

📜 Certifications

🏆 NVIDIA – Building RAG Agents with LLMs
🏆 Google Cloud – Data Engineering Foundations
🏆 AWS – Machine Learning Specialty
🏆 Microsoft – MERN Stack Development
🏆 Snowflake – End-to-End Data Engineering
🏆 Coursera – Machine Learning Specialization
🔗 View All Credentials

📚 Research Publications

IEEE BIBM 2024 – “Leveraging ML for Predicting Circadian Transcription in mRNAs and lncRNAs”
DOI: 10.1109/BIBM62325.2024.10822684
MLCB – “Harnessing DNA Foundation Models for TF Binding Prediction in Plants”

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