Ravi Thej Neeli

About Ravi Thej Neeli

Built production-grade Machine Learning and GenAI systems that are reliable, governed, observable, and scalable. With 8+ years of experience across ML engineering, data science, and platform engineering, I specialize in taking ML/LLM capabilities from prototype → production by pairing strong modeling with robust MLOps, Infrastructure-as-Code, and operational excellence. My focus is building structured agentic systems, evaluation-driven RAG pipelines, and end-to-end ML platforms where quality is measurable, deployments are reproducible, and systems are safe under real-world constraints.What I Do • Architect tool-governed agentic systems where LLMs orchestrate and deterministic tools compute — minimizing hallucinations and enforcing correctness • Build RAG pipelines with embedding services, vector databases, caching layers, and retrieval quality evaluation • Create evaluation frameworks (accuracy, tool selection F1, grounding/hallucination detection, efficiency, reasoning quality) and wire them into CI/CD as release gates • Own MLOps workflows: automated retraining, drift feedback loops, model versioning, and production monitoring • Design Infrastructure-as-Code (IaC) for AI platforms using Terraform + CloudFormation to provision secure, environment-agnostic deployments • Build scalable backend services and microservices (synchronous + async), containerize with Docker, and deploy via Kubernetes/EKS/ECS/Lambda • Implement observability-first systems using OpenTelemetry tracing + structured logging + production dashboards • Engineer data pipelines and analytics workflows (Spark/PySpark, Databricks streaming + dashboards)Technical Toolkit GenAI / Agentic Systems: LangGraph, LangChain, Strands SDK, tool calling, prompt engineering, RAG, embeddings, vector search, query rewriting, grounding + safety evals ML / NLP / DL: ensembles, boosting, text mining, semantic similarity, time series, object detection, NER MLOps / Platform: CI/CD pipelines, automated retraining, drift monitoring, model evaluation gates, Docker, Kubernetes, EKS/ECS/Lambda, OpenTelemetry, CloudWatch IaC / Cloud: Terraform, CloudFormation, AWS (S3, SageMaker, OpenSearch, Neptune, Lambda, ECS, EKS), Azure OpenAI Data / Analytics: Databricks, PySpark, Spark SQL, Hive, Kinesis streaming, Tableau/Power BI

Experience

1. Machine Learning Engineer

   ADP

   Feb 2022 — Present · San Francisco, CA, US

Education

• Northeastern University

  Master's degree, Data Analytics

  2018 — 2019

• Shanmugha Arts, Science, Technology and Research Academy

  Bachelor of Technology, Computer Science

  2011 — 2015