Swetha Nallamilli

About Swetha Nallamilli

Senior Applied Machine Learning Engineer & Researcher with 17+ years of experience, focused on applied ML research in LLMs, agentic AI systems, and fine-tuning strategies. Passionate about translating cutting-edge research into scalable, production-ready AI solutions across security and cloud platforms.Proven expertise in building and deploying LLM-powered, ML-driven systems using Azure and AWS, with strong foundations in MLOps, AIOps, and DevOps. Known for technical leadership, mentoring, and driving measurable business impact through data-driven innovation.Core CompetenciesApplied ML Research & InnovationLeading applied research initiatives in LLMs, agentic workflows, model fine-tuning (SFT, PEFT, RLHF), retrieval-augmented generation (RAG), semantic reasoning, and feedback-aware learning systems to solve real-world security and reliability challenges.Technical Leadership & Engineering ExcellenceDemonstrated strength in technical leadership, architectural decision-making, coaching, and mentoring, enabling teams to deliver high-quality, reliable ML systems while fostering a culture of engineering excellence and continuous learning.Advanced Machine Learning & Statistical ModelingDeep expertise across supervised, unsupervised, and reinforcement learning, probabilistic and statistical models, clustering techniques (K-means, hierarchical), ensemble methods, forecasting, anomaly detection, recommendation systems, and evaluation-driven model iteration.Deep Learning & Language ModelsHands-on experience with CNNs, RNNs, Transformers, foundation models, LLMs, semantic search, contextual mining, and multi-variant analysis, with strong grounding in A/B testing, model versioning, validation, and performance evaluation.Cross-Functional Collaboration & Business AlignmentPartnering effectively with product managers, engineers, and business leaders to identify high-impact problems, translate business requirements into ML solutions, and communicate complex technical concepts to non-technical stakeholders.Agile Delivery & Outcome OwnershipLeading sprint teams with clarity and purpose, providing technical direction while driving measurable business outcomes through iterative experimentation and continuous feedback loops.Research PublicationsAuthor of peer-reviewed research published in the Microsoft Journal of Applied Research (MSJAR), including:Enhancing Anomaly Detection through Machine Learning and Expert FeedbackDetecting Anomalous User Behavior in Cybersecurity Using Machine LearningSite Outage Correlation Using Machine Learning

Experience

1. Senior Applied Machine Learning Engineer

   Microsoft

   Feb 2022 — Present · Hyderabad, IN

Education

• Jawaharlal Nehru Technological University

  Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering

  2000 — 2004

• Birla Institute of Technology and Science, Pilani

  M.Tech, Data Science and Data Engineering

Skills

• Nhibernate
• Agile Methodologies
• Javascript
• Node.js
• Express.js
• Localization
• Requirements Analysis
• Jasmine Framework
• Logstash
• Fxcop
• Inversion of Control (Ioc)
• .net Framework
• C#
• Xml
• Representational State Transfer (Rest)
• Test Driven Development
• Continuous Integration
• Oracle Database
• Wcf
• Microservices
• Microsoft Sql Server
• Web Services
• Wpf
• Software Development Life Cycle (Sdlc)
• Amazon Web Services (Aws)
• Software Project Management
• Asp.net Mvc
• Angularjs
• Ado.net
• Globalization
• Sql
• Production Deployment
• Asp.net
• Service-Oriented Architecture (Soa)
• Language Integrated Query (Linq)
• Software Development
• Fiddler