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Senior Generative AI & LLMOps Engineer with nearly 10 years of experience designing, deploying, and scaling production-grade AI systems, with a focus on agentic AI, LLM evaluation, and RAG reliability. Skilled in bridging applied ML with distributed systems to deliver resilient, cost-efficient, and observable generative AI solutions. Experienced in medical document intelligence and automation pipelines, including OCR and layout-aware chunking, and in building CI/CD release gates and evaluation frameworks using MLflow and Langfuse. Also delivers business intelligence and analytics using SQL and dashboarding tools to turn complex data into actionable insights.…Senior Generative AI & LLMOps Engineer with nearly 10 years of experience designing, deploying, and scaling production-grade AI systems, with a focus on agentic AI, LLM evaluation, and RAG reliability. Skilled in bridging applied ML with distributed systems to deliver resilient, cost-efficient, and observable generative AI solutions. Experienced in medical document intelligence and automation pipelines, including OCR and layout-aware chunking, and in building CI/CD release gates and evaluation frameworks using MLflow and Langfuse. Also delivers business intelligence and analytics using SQL and dashboarding tools to turn complex data into actionable insights.

Biswas Nandamuri

AI Engineer, Data Scientist, AI Developer, +3

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Senior Generative AI & LLMOps Engineer with nearly 10 years of experience designing, deploying, and scaling production-grade AI systems, with a focus on agentic AI, LLM evaluation, and RAG reliability. Skilled in bridging applied ML with distributed systems to deliver resilient, cost-efficient, and observable generative AI solutions. Experienced in medical document intelligence and automation pipelines, including OCR and layout-aware chunking, and in building CI/CD release gates and evaluation frameworks using MLflow and Langfuse. Also delivers business intelligence and analytics using SQL and dashboarding tools to turn complex data into actionable insights.…Senior Generative AI & LLMOps Engineer with nearly 10 years of experience designing, deploying, and scaling production-grade AI systems, with a focus on agentic AI, LLM evaluation, and RAG reliability. Skilled in bridging applied ML with distributed systems to deliver resilient, cost-efficient, and observable generative AI solutions. Experienced in medical document intelligence and automation pipelines, including OCR and layout-aware chunking, and in building CI/CD release gates and evaluation frameworks using MLflow and Langfuse. Also delivers business intelligence and analytics using SQL and dashboarding tools to turn complex data into actionable insights.

Available to hire

Senior Generative AI & LLMOps Engineer with nearly 10 years of experience designing, deploying, and scaling production-grade AI systems, with a focus on agentic AI, LLM evaluation, and RAG reliability. Skilled in bridging applied ML with distributed systems to deliver resilient, cost-efficient, and observable generative AI solutions.

Experienced in medical document intelligence and automation pipelines, including OCR and layout-aware chunking, and in building CI/CD release gates and evaluation frameworks using MLflow and Langfuse. Also delivers business intelligence and analytics using SQL and dashboarding tools to turn complex data into actionable insights.

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Work Experience

AI Engineer at Veritec AI

November 1, 2025 - Present

Architected and developed autonomous AI agent systems for medical document intelligence using LangGraph and Model Context Protocol (MCP) with Vertex AI Gemini. Built stateful, human-in-the-loop workflows with guardrailed production deployment patterns. Engineered event-driven medical document pipelines on Azure Kubernetes Service using Service Bus-triggered KEDA ScaledJobs and Dockerized Go/Python services for OCR, billing, medical events, chat, indexing, and drafting. Designed production-grade medical RAG pipelines using LanceDB with dense embeddings, metadata filtering, hybrid retrieval concepts, re-ranking, and vision enrichment to improve retrieval quality from OCR and medical artifacts. Led LLM/ML evaluation strategy using MLflow and Langfuse datasets (golden test sets, hold-out splits, evaluator scoring) with CI/CD release gates to benchmark recall, accuracy, latency, and cost. Improved medical event extraction recall from 77.1% to 82.5% and reduced missed medication recognition

AI Engineer (formerly ML Engineer) at Trivie Inc. (acquired by Quantum5)

December 1, 2022 - October 1, 2025

Shipped LLM-powered content generation features that reduced authoring time from hours to minutes using FastAPI and LangChain with map-reduce summarization and DSPy prompt optimization plus automated quality feedback loops. Designed and deployed BI solutions including 15+ Tableau dashboards and an SQL-based analytics platform serving 5K+ daily users, replacing a legacy 10-minute aggregation system using PostgreSQL materialized views and BigQuery optimizations. Built automated reporting with Advanced Excel (Power Query, Pivot Tables, formulas) eliminating ~15 hours of manual work weekly and reducing entry errors by 90%. Implemented observability and evaluation hooks into LangGraph agents with latency tracking, validation, and auto-retries, reducing silent agent failures by 65%. Reduced LLM API costs by half and increased throughput 4x using async FastAPI, Redis caching, and intelligent routing between GPT-3.5/GPT-4/Claude models. Optimized RAG latency using multi-stage retrieval (FAIS

Graduate Research Assistant at Data Institute of Societal Challenges (University of Oklahoma)

February 1, 2022 - December 1, 2022

Co-led SMART (Social Media Analytics and Reporting Tool) funded by NATO to analyze correlations between IoT sensor data and social media sentiment for real-time situational awareness. Built a FastAPI + React backend for interactive visualization using Tableau-style dashboards. Developed PySpark/Databricks pipelines to ingest and process text and sensor data with BigQuery and GCP Cloud Storage. Applied transformer models (BERT, BART, T5) for topic detection and summarization, generating concise daily insights. Implemented SpaCy-based entity redaction for PII removal and GDPR compliance. Used MLflow with Docker-based experiment tracking for reproducibility and dataset versioning, and contributed to technical documentation on multimodal AI for environmental event detection.

Data Scientist at Techniecode Inc.

October 1, 2020 - December 1, 2021

Analyzed CRM data across 50,000+ customer records using SQL and Advanced Excel to identify lead scoring patterns and behavior trends, improving sales conversion by 12%. Built ML-assisted lead scoring and recommendation models using scikit-learn and Pandas; applied logistic regression, random forest, and gradient boosting with class-imbalance handling (SMOTE and weighting) to improve precision-recall by ~12%. Designed SQL-based ETL pipelines in PostgreSQL to unify CRM, loan, and financial datasets for downstream ML and BI. Created Power BI dashboards and Excel analytics exposing ML output metrics, improving stakeholder decision-making speed by 45%. Partnered with Sales/Marketing/Finance to translate questions into analytical frameworks and delivered recurring weekly insights. Refactored legacy systems into containerized microservices, improved data refresh cycles from hours to minutes, and strengthened deployment reliability and operational uptime.

Junior Data Scientist at NexaTech Systems (FinTech Analytics)

October 1, 2016 - September 1, 2020

Supported Tableau dashboards and SQL reporting for financial analytics including fraud trends, risk scoring, churn prediction, and portfolio performance. Assisted with risk scoring, fraud detection, and churn modeling using structured financial, transactional, and customer behavior data. Prepared and validated datasets using SQL, Python, Pandas, and Excel for EDA and ML. Contributed to ETL and feature engineering workflows using PostgreSQL, Parquet, Luigi, and Apache Spark. Built Excel-based templates for reporting and scenario analysis, and helped develop/test Flask API endpoints to expose model outputs. Supported reusable feature dataset creation and contributed to tuning and evaluating models using GridSearchCV/RandomizedSearchCV. Participated in ML experiment tracking, documentation, and operational monitoring (Grafana-style) for data quality and pipeline status.