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I am Raghu Ram Sai Ammula, an AI/ML engineer specializing in production-grade LLM and ML systems across E-Commerce and enterprise domains. I design scalable RAG pipelines, personalized recommendations, and AI-driven experiences using Python, FastAPI, and cloud platforms. I thrive on turning AI innovations into business impact, with strong MLOps expertise spanning CI/CD, MLflow, Airflow, Docker, and Kubernetes, and a track record of measurable improvements in engagement, conversion, and reliability.…I am Raghu Ram Sai Ammula, an AI/ML engineer specializing in production-grade LLM and ML systems across E-Commerce and enterprise domains. I design scalable RAG pipelines, personalized recommendations, and AI-driven experiences using Python, FastAPI, and cloud platforms. I thrive on turning AI innovations into business impact, with strong MLOps expertise spanning CI/CD, MLflow, Airflow, Docker, and Kubernetes, and a track record of measurable improvements in engagement, conversion, and reliability.

Raghu Ram Sai Ammula

AI Engineer, Data Scientist, Full Stack Developer

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I am Raghu Ram Sai Ammula, an AI/ML engineer specializing in production-grade LLM and ML systems across E-Commerce and enterprise domains. I design scalable RAG pipelines, personalized recommendations, and AI-driven experiences using Python, FastAPI, and cloud platforms. I thrive on turning AI innovations into business impact, with strong MLOps expertise spanning CI/CD, MLflow, Airflow, Docker, and Kubernetes, and a track record of measurable improvements in engagement, conversion, and reliability.…I am Raghu Ram Sai Ammula, an AI/ML engineer specializing in production-grade LLM and ML systems across E-Commerce and enterprise domains. I design scalable RAG pipelines, personalized recommendations, and AI-driven experiences using Python, FastAPI, and cloud platforms. I thrive on turning AI innovations into business impact, with strong MLOps expertise spanning CI/CD, MLflow, Airflow, Docker, and Kubernetes, and a track record of measurable improvements in engagement, conversion, and reliability.

Available to hire

I am Raghu Ram Sai Ammula, an AI/ML engineer specializing in production-grade LLM and ML systems across E-Commerce and enterprise domains. I design scalable RAG pipelines, personalized recommendations, and AI-driven experiences using Python, FastAPI, and cloud platforms.

I thrive on turning AI innovations into business impact, with strong MLOps expertise spanning CI/CD, MLflow, Airflow, Docker, and Kubernetes, and a track record of measurable improvements in engagement, conversion, and reliability.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

AI Engineer at Walmart

September 1, 2024 - Present

Built and fine‑tuned LLM‑driven recommendation systems (GPT‑4 via Azure OpenAI, Llama 3 with LoRA/QLoRA) to deliver hyper‑personalized product experiences; increased engagement by 37% and boosted conversion across e‑commerce storefronts. Architected end‑to‑end RAG pipelines on Azure (LangChain + FAISS + Azure Cognitive Search), integrating multi‑source embeddings and context re‑ranking; improved semantic search accuracy by 42% and reduced irrelevant matches. Operationalized LLM workflows on Azure ML, Azure Functions, and AKS with autoscaling, containerized deployment, and Redis caching; cut inference latency by 31% and achieved 99.9% uptime for real‑time recommendations. Orchestrated model monitoring and retraining via Azure ML Pipelines, MLflow, and Application Insights dashboards; automated drift detection and pipeline triggers, sustaining >95% response faithfulness during traffic spikes. Developed and deployed Generative AI chatbots with FastAPI + Azure OpenAI AP

Machine Learning Engineer at DXC Technology

June 1, 2021 - August 1, 2023

Developed and deployed large-scale recommendation systems via collaborative filtering and gradient boosting on Amazon SageMaker; boosted CTR by 21% and engagement across e‑commerce platforms. Designed distributed ETL pipelines with AWS Glue, Apache Spark, and Airflow to process multi‑terabyte user interactions; achieved 99.8% ingestion accuracy and reduced data latency from hours to minutes. Implemented NLP models (BERT, T5) for semantic search and product categorization; improved search relevance by 28% and enabled contextual product discovery for thousands of SKUs. Designed and deployed real-time fraud detection pipelines with Amazon Kinesis and anomaly detection; reduced false positives by 22% and prevented major financial exposure across transactional streams. Automated the end-to-end ML lifecycle with MLflow, Docker, and AWS Lambda for model versioning, evaluation, and retraining; integrated CI/CD and observability via CloudWatch, S3 versioning, and metric dashboards, achievin