Ruthwik Reddy Adapala
Software Development Engineer • United States • r**************@gmail.com • +15******292 • linkedin.com/••••• • github.com/•••••
Professional Summary
Software Development Engineer with 2+ years of experience developing AI powered software, Generative AI, computer vision, and scalable machine learning solutions. Expertise in Python, PyTorch, TensorFlow, LangChain, RAG, and cloud platforms, with a proven record of improving model accuracy by 12% through production ready AI systems. Delivers intelligent, high performance applications by combining software engineering, data engineering, and modern AI technologies to solve complex business challenges while building scalable, secure, and production ready AI solutions.
Technical Skills
Programming Languages: Python
Backend Development & APIs: FastAPI,REST APIs
Databases: SQL,MongoDB,NoSQL
AI & ML Frameworks: PyTorch,TensorFlow,Scikit-learn,XGBoost
Generative AI & LLMs: LangChain,LangGraph,Hugging Face Transformers,LLM Fine-Tuning,LoRA,QLoRA,RAG,Multimodal Models,vLLM,MCP
Computer Vision & Deep Learning: OpenCV,Image Segmentation,Object Detection,UNet,NeRF,EfficientNet,Diffusion Models,Transfer Learning
Vector Databases & NLP: FAISS,Pinecone,Sentence-BERT,DistilBERT,Clinical NLP
Big Data & Data Engineering: Apache Spark,Hadoop,Distributed Computing,ETL Pipelines
Cloud Platforms: AWS,Microsoft Azure,Google Cloud Platform
Containerization & Performance Profiling: Docker,Kubernetes,PyTorch Profiler,Nsight Systems
Work Experience
Leap of Faith Technologies
Chicago, IL
Software Development Engineer
Jun 2025 – Dec 2025
Tech Stack: OpenMRS, REST APIs, LangChain, Embeddings, Vector Search, Python, Machine Learning, SQL, Clinical AI, Retrieval-Augmented Generation
- Integrated OpenMRS with enterprise healthcare applications using REST APIs, enabling secure end-to-end medication management workflows for 1,000+ active users across clinical systems.
- Developed AI-powered clinical assistant solutions using LangChain, RAG and Python to automate drug information retrieval, prescription reminders, and patient adherence tracking, improving clinical support services.
- Designed and optimized complex SQL queries and stored procedures to process 100K+ clinical records, improving data retrieval efficiency by 30% for AI-driven reporting.
- Collaborated with a cross functional team of software engineers, AI developers, and healthcare specialists to deliver production ready healthcare AI features, contributing to sprint deliveries and stakeholder acceptance.
- Implemented automated medical billing and claims workflows using Python, Machine Learning, REST APIs, and FAISS, reducing manual processing effort by 35% across patient accounts.
- Optimized AI driven medication adherence solutions through continuous model enhancements, increasing patient adherence by 25% while improving overall clinical workflow efficiency.
NSL Hub
Hyderabad, India
Machine Learning Engineer
May 2021 – Dec 2023
Tech Stack: Python, PyTorch, TensorFlow, OpenCV, NeRF, AWS EC2, Computer Vision, Deep Learning, Image Segmentation, Model Fine-Tuning, Model Deployment
- Architected a real time driver monitoring pipeline using computer vision models, improving detection accuracy by 12% while meeting production latency requirements.
- Optimized image segmentation and deep learning models for indoor vision applications, enabling scalable inference workflows on AWS EC2 for high volume image processing.
- Refined model performance by applying transfer learning, hyperparameter tuning, and fine tuning techniques, increasing inference efficiency across computer vision workloads.
- Spearheaded collaboration with AI engineers, software developers, and product teams to validate model performance, accelerating production deployment across vision based solutions.
- Leveraged Python, PyTorch, TensorFlow, OpenCV, NeRF to build scalable computer vision pipelines, improving model deployment efficiency and system reliability.
Education
Illinois Institute of Technology
Master of Science in Artificial Intelligence • Chicago, IL • Jan 2024 – Dec 2025
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