D
Dada Khalandar Puletipalli
About
Detail
North Carolina, United States
Résumé
Jobs
verified_user
0% verified
AI Software EngineerHugging FaceDec 2024 - Current (1 year 8 months)• Partnered with product managers and research teams to define Al product roadmaps and translate experimental models into user-facing features, improving product usability and feature adoption. • Integrated transformer and multimodal models into product workflows using APIs, vector databases, and system design patterns to enable scalable and seamless user experiences. • Engineered backend services for Al-driven product features using Python, REST/gRPC, Docker, and Kubernetes, ensuring consistent performance across production environments. • Established observability frameworks with logging, metrics, and tracing to monitor product performance and optimize infrastructure efficiency, reducing operational costs by 20%. • Enhanced open-source li- Machine Learning EngineerCognizantJul 2022 - Jul 2023 (1 year 1 month)• Built and fine-tuned machine learning models for prediction and classification use cases, enabling reliable product features and improving model accuracy by 18%. • Structured large-scale datasets using Python and SQL to support product analytics and feature development, ensuring consistent and high-quality data pipelines. • Created REST APIs with authentication and input validation to power real-time product features, enabling secure and responsive user-facing applications. • Deployed ML services using Docker and Kubernetes to support scalable product functionality, streamlining release cycles and ensuring stable feature delivery. • Applied LLM-based solutions with prompt safety and guardrails to enhance Al-driven product capabilities whi
- Software DeveloperFreshworksAug 2021 - Jun 2022 (11 months)• Developed backend services using object-oriented principles, improving response time by 20% while adhering to secure coding standards and minimizing exposure of user data in application workflows. • Implemented REST APIs using the Freshworks Developer Platform (FDP), incorporating authentication and access control to reduce manual workflow effort by 25% and protect sensitive user interactions. • Contributed to scalable application modules with clean and maintainable code, reducing debugging effort and mitigating potential data security vulnerabilities. • Collaborated with QA teams to support automated testing, including security and regression validation, increasing coverage and reducing post- deployment issues by 15%. • Assisted in build
Education
verified_user
0% verified
- AAWS academy cloud foundationsAWS Academy Graduate
- MAzure AI fundamentalsMicrosoft Certified
- MAzure fundamentalsMicrosoft Certified
- Master of Science (Cyber Security)University of Central MissouriMO, USA
- Bachelor of Engineering in Computer Science (Software Modelings & Devops)KL UniversityIndia
Projects (professional or personal)
verified_user
0% verified
- PPrivacy-Preserving Machine Learning• Explored privacy-preserving machine learning techniques by implementing a federated learning setup to simulate decentralized model training across distributed datasets. • Conducted experiments with differential privacy by introducing noise mechanisms during training, reducing simulated sensitive data exposure risk by 20-25%. • Analyzed trade-offs between model performance and privacy guarantees, evaluating the impact on accuracy, data utility, and potential data leakage risks.
- AAI-Powered Anomaly Detection & Monitoring System• Developed an anomaly detection system using Python, ML models, and Grafana dashboards to monitor system logs and detect irregular patterns in real time. • Implemented data pipelines and model evaluation workflows to improve detection accuracy and support automated alert generation. • Decreased incident response time by 35% by enabling early identification of abnormal system behaviour.
- LLLM-Based Secure Document Intelligence System• Built a RAG pipeline using LangChain, FAISS, and FastAPI to retrieve and summarize enterprise documents, enabling structured query-based insights from unstructured data. • Integrated authentication, rate limiting, and prompt validation to ensure secure inference and controlled access to sensitive information. • Reduced manual document search effort by 40% through optimized retrieval and response generation workflows.