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| # 👋 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**. | |
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| ## 🎯 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 | |
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| ## 🛠️ 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](https://github.com/krishna-creator/Real-Time-IoT-Based-Temperature-Analytics-and-Forecasting) | |
| ### 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](https://github.com/krishna-creator/ProxytuNER) | |
| ### 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](https://github.com/krishna-creator/SE-Project---IntelliMeet) | |
| ### 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](https://www.linkedin.com/in/krishnavamsidhulipalla/) | |
| --- | |
| ## 📚 Research Publications | |
| - **IEEE BIBM 2024** – “Leveraging ML for Predicting Circadian Transcription in mRNAs and lncRNAs” | |
| [DOI: 10.1109/BIBM62325.2024.10822684](https://doi.org/10.1109/BIBM62325.2024.10822684) | |
| - **MLCB** – “Harnessing DNA Foundation Models for TF Binding Prediction in Plants” | |
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| ## 🔗 External Links / Contact details | |
| - 🌐 [Personal Portfolio/ personal website](http://krishna-dhulipalla.github.io) | |
| - 🧪 [GitHub](https://github.com/Krishna-dhulipalla) | |
| - 💼 [LinkedIn](https://www.linkedin.com/in/krishnavamsidhulipalla) | |
| - 📬 dhulipallakrishnavamsi@gmail.com | |
| - 🤖 [Personal Chatbot](https://huggingface.co/spaces/krishnadhulipalla/Personal_ChatBot) | |