# Jaswanth Nalluri

> Senior Machine Learning Engineer | LLMs, RAG, Agentic AI | Scalable AI Systems | AWS & GCP | Ex-Scale AI, Alphabet

Location: New York City Metropolitan Area, United States
Profile: https://flows.cv/jaswanthnalluri

Machine Learning Engineer with 5+ years of experience building production-scale AI systems, specializing in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and autonomous AI agents.
At Scale AI and Alphabet, I have designed and deployed enterprise AI systems that improve model reliability, retrieval accuracy, and real-world task performance. My work includes building RAG pipelines with vector databases, fine-tuning transformer models using RLHF, and developing evaluation frameworks to reduce hallucinations and improve response quality.

I have hands-on experience developing scalable ML systems using Python, FastAPI, Docker, and Kubernetes, and deploying distributed inference pipelines on AWS and GCP. My work also includes multimodal AI systems, reinforcement learning for intelligent agents, and large-scale data pipelines for model training and evaluation.
Key areas of expertise:

• LLMs, RAG architectures, and prompt engineering
• Agentic AI systems and reinforcement learning
• Scalable ML pipelines and distributed inference
• MLOps, cloud infrastructure (AWS, GCP), and model deployment

I focus on building reliable, scalable AI systems that translate complex data into real-world intelligent applications.

## Work Experience
### Machine Learning Engineer @ Scale AI
Jan 2025 – Present | New York, United States
Working on production-scale LLM systems, RAG pipelines, and enterprise AI platforms with a focus on reliability, scalability, and evaluation.

●Designed and deployed RAG pipelines integrating vector databases and LLM APIs, improving retrieval accuracy by 32% and reducing hallucinations by 27%
●Fine-tuned transformer-based LLMs using domain-specific datasets and RLHF, increasing response relevance by 35%
●Built automated LLM evaluation frameworks measuring hallucination, latency, and answer quality, improving reliability by 30%
●Developed scalable AI microservices using Python, FastAPI, Docker, and Kubernetes for production inference systems
●Engineered RAG architectures using LangChain, LlamaIndex, Pinecone, and FAISS for semantic search
Implemented distributed ML pipelines using PyTorch, Hugging Face, and Ray Serve for GPU-based inference
●Designed monitoring systems using Prometheus and Grafana for model observability and performance tracking

### Machine Learning Engineer @ Alphabet Inc.
Jan 2025 – Jan 2025 | San Francisco, CA
Built autonomous AI agents and multimodal systems for web interaction and task execution using reinforcement learning and LLMs.

●Designed autonomous AI agents for multi-step web tasks, improving task completion accuracy by 34%
●Developed multimodal pipelines combining vision transformers and LLMs for UI understanding
●Implemented hierarchical planning systems converting natural language into structured actions, reducing failures by 27%
●Applied reinforcement learning (PPO, actor-critic) to optimize agent behavior in dynamic environments
●Built scalable data pipelines for large-scale web interaction datasets
●Optimized distributed training and inference on GCP with TPU infrastructure
●Deployed models using Vertex AI with monitoring and automated retraining pipelines

### Data Analyst @ Accenture
Jan 2020 – Jan 2023 | India
Developed NLP and computer vision pipelines for document processing, automation, and fraud detection in financial systems.

●Built OCR + NLP pipelines extracting structured data from financial documents, reducing manual effort by 70%
●Developed document classification and entity extraction models improving accuracy by 35%
●Designed fraud detection validation rules enabling real-time anomaly detection
●Engineered ML pipelines using TensorFlow, PyTorch, OpenCV, and spaCy
●Deployed containerized ML services using Docker, FastAPI, Kubernetes, and AWS
●Implemented end-to-end MLOps workflows, including CI/CD and model monitoring


## Education
### Master of Science in Computer Science
University of North Texas


## Contact & Social
- LinkedIn: https://linkedin.com/in/njc77

---
Source: https://flows.cv/jaswanthnalluri
JSON Resume: https://flows.cv/jaswanthnalluri/resume.json
Last updated: 2026-04-17