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AI/ML Engineer with 9+ years of experience building and deploying scalable cloud-based microservices, data analytics, ML, and Generative AI solutions. Skilled in LLM application development using GPT-4, Llama, BERT, and Azure OpenAI, including RAG frameworks with FAISS and context-aware retrieval. Experienced in orchestrating LLM agents for automation, structured extraction, and intelligent workflows, plus developing Text-to-SQL systems. Strong MLOps background across Docker, Kubernetes, CI/CD, and end-to-end ML lifecycle on AWS/Azure/Databricks, with leadership experience delivering enterprise-grade AI aligned to business outcomes.…AI/ML Engineer with 9+ years of experience building and deploying scalable cloud-based microservices, data analytics, ML, and Generative AI solutions. Skilled in LLM application development using GPT-4, Llama, BERT, and Azure OpenAI, including RAG frameworks with FAISS and context-aware retrieval. Experienced in orchestrating LLM agents for automation, structured extraction, and intelligent workflows, plus developing Text-to-SQL systems. Strong MLOps background across Docker, Kubernetes, CI/CD, and end-to-end ML lifecycle on AWS/Azure/Databricks, with leadership experience delivering enterprise-grade AI aligned to business outcomes.

Spoorthi Ramireddygari

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with 9+ years of experience building and deploying scalable cloud-based microservices, data analytics, ML, and Generative AI solutions. Skilled in LLM application development using GPT-4, Llama, BERT, and Azure OpenAI, including RAG frameworks with FAISS and context-aware retrieval. Experienced in orchestrating LLM agents for automation, structured extraction, and intelligent workflows, plus developing Text-to-SQL systems. Strong MLOps background across Docker, Kubernetes, CI/CD, and end-to-end ML lifecycle on AWS/Azure/Databricks, with leadership experience delivering enterprise-grade AI aligned to business outcomes.…AI/ML Engineer with 9+ years of experience building and deploying scalable cloud-based microservices, data analytics, ML, and Generative AI solutions. Skilled in LLM application development using GPT-4, Llama, BERT, and Azure OpenAI, including RAG frameworks with FAISS and context-aware retrieval. Experienced in orchestrating LLM agents for automation, structured extraction, and intelligent workflows, plus developing Text-to-SQL systems. Strong MLOps background across Docker, Kubernetes, CI/CD, and end-to-end ML lifecycle on AWS/Azure/Databricks, with leadership experience delivering enterprise-grade AI aligned to business outcomes.

Available to hire

AI/ML Engineer with 9+ years of experience building and deploying scalable cloud-based microservices, data analytics, ML, and Generative AI solutions. Skilled in LLM application development using GPT-4, Llama, BERT, and Azure OpenAI, including RAG frameworks with FAISS and context-aware retrieval.

Experienced in orchestrating LLM agents for automation, structured extraction, and intelligent workflows, plus developing Text-to-SQL systems. Strong MLOps background across Docker, Kubernetes, CI/CD, and end-to-end ML lifecycle on AWS/Azure/Databricks, with leadership experience delivering enterprise-grade AI aligned to business outcomes.

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Work Experience

Senior Software Engineer – AI/ML Operations at BCBS, USA

August 1, 2024 - Present

Developed an AI-driven insurance analytics platform leveraging Machine Learning, NLP, and Generative AI, reducing claims processing time by 30–35% and improving underwriting decision accuracy by 15–20%. Implemented scalable MLOps pipelines on Azure using Azure Machine Learning and Cognitive Services for model deployment, monitoring, and retraining. Built conversational AI solutions using ChatGPT API for customer engagement and self-service resolution. Applied prompt engineering for claims processing and underwriting workflows. Fine-tuned large language models using PEFT and LoRA on Azure ML for insurance-specific use cases. Built generative AI pipelines to extract insights from unstructured insurance documents and enhanced retrieval workflows using LangChain and FAISS-based RAG, improving retrieval accuracy by 25% and reducing response latency by 40%. Deployed models using Azure DevOps CI/CD with Terraform and Docker while maintaining compliance. Collaborated cross-functionally to

Software Engineer – AI/ML Operations at Walmart-US

August 1, 2023 - July 31, 2024

Implemented MLOps pipelines using CI/CD, Docker, and Kubernetes, reducing deployment time by 50% and increasing release reliability. Utilized MLflow for experiment tracking, model versioning, and lifecycle management. Managed end-to-end deployment of machine learning and Generative AI models with scalability and reliability. Developed/optimized data pipelines reducing ETL processing time by 35% while improving data quality. Integrated LLM-based solutions including Text-to-SQL to enable natural language interaction with enterprise data platforms, using prompt engineering to improve response accuracy. Worked with AWS and Azure services for training, deployment, and large-scale data processing. Built and maintained ETL pipelines using Azure Data Factory. Monitored model performance and implemented improvements to reduce drift and maintain accuracy.

Associate Manager – Data Science at Nike-US

October 1, 2020 - July 31, 2023

Developed AI algorithms for advertisement analysis using TensorFlow and PyTorch, automating large-scale image extraction and model training workflows. Used Automation Anywhere and RPA to streamline ad data collection and demographic data pipelines, improving processing efficiency. Built and deployed fraud detection models improving detection accuracy by 18% and reducing false positives by 20%. Implemented MLOps using CI/CD, Docker, Kubernetes, and model versioning on Azure. Developed Generative AI-driven Text-to-SQL solutions for natural language access to enterprise databases. Processed and analyzed structured/unstructured datasets using Spark and Pandas for feature extraction and transformation. Designed ETL pipelines using Azure Data Factory, Python, and SQL; built supporting infrastructure using Azure Synapse Analytics, Azure Blob Storage, and Azure SQL. Optimized SQL queries and triggers to reduce latency. Developed prompt engineering strategies to reduce hallucinations and improv

Data Scientist at Nisum, Hyderabad, India

June 1, 2016 - July 31, 2019

Developed recommendation models improving user engagement by 25% and increasing recommendation accuracy by 20%. Designed personalized learning/recommendation engines using AWS SageMaker and Python. Built scalable ETL pipelines with AWS Glue and Lambda for student interaction data ingestion/processing. Automated processing for millions of records using SQL, Python, Pandas, and Scikit-learn. Used AWS S3 and Redshift for storage and querying of structured and unstructured datasets. Built referral and peer-learning recommendation logic and personalized learning pathways in Byju’s LMS. Developed predictive analytics for learning outcome forecasting and targeted remedial actions. Designed database structures (normalized/denormalized) to optimize query performance for large-scale data and used advanced data structures/modeling (e.g., decision trees, graph-based approaches). Integrated REST APIs for real-time recommendations in the mobile app and analyzed behavior to refine engagement and pr