Vamshi Krishna Chilumula
Machine Learning Engineer | Expertise in LLM Fine-Tuning (LLaMA 2, Mistral), RAG Pipelines, MLOps (Docker, Kubernetes, Jenkins), and Scalable AI Solutions on AWS & GCP | Netflix & Cognizant
- Role
- Machine Learning Engineer at Netflix
- Location
- Dallas, TX, US
- LinkedIn followers
- 500 followers
About Vamshi Krishna Chilumula
Machine Learning Engineer with 4+ years of experience designing and deploying scalable…
Experience
Machine Learning Engineer
Sep 2024 — Present · CA, US
Developed a serverless feature store using AWS Lambda, DynamoDB, and S3 to centralize real-time user embeddings, reducing feature duplication by 35% and enhancing model freshness via EventBridge-triggered updates. • Fine-tuned LLMs (LLaMA 2, Mistral) for personalized content summarization using LoRA adapters, cutting training costs by 45% and improving summary accuracy by 30%, validated by human evaluators.• Designed A/B testing frameworks for RAG-enhanced recommendation systems, implementing statistical significance monitoring and multi-armed bandit optimization that improved content discovery metrics by 22%. • Engineered containerized model serving systems with Docker and Kubernetes, and integrated CI/CD pipelines using Jenkins, enabling zero-downtime deployments across global environments.• Created scalable ML tooling in Python with NumPy, PyTorch, and Ray for automated feature validation and model monitoring, reducing incidents by 40% and accelerating experiment cycles by 25%.• Developed NLP models using BERT and spaCy to improve semantic matching and content comprehension for international content, resulting in a 10% boost in non-English title relevance.• Built real-time feature processing pipeline using Apache Flink and Kafka, enabling sub-500ms latency on user interaction events (clicks, skips, watch time), delivering instant personalization to 5M+ users.
Education
University of North Texas
Master's degree
2023 — 2025
Jawaharlal Nehru Technological University
Bachelor of Technology - BTech
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