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I am Sunil Kumar Medara, a NVIDIA Certified GenAI Engineer with 6+ years of building production-grade ML, GenAI, and data engineering solutions across healthcare, nonprofit, financial services, and logistics. I specialize in LLM-powered applications, RAG pipelines, and agentic AI workflows, delivering scalable AWS architectures and MLOps practices that drive measurable business impact. I enjoy translating complex data problems into secure, reliable AI systems, leading cross-functional teams, and delivering dashboards that tell the story behind the numbers. My hands-on experience spans from model development to real-time inference and governance, ensuring robust, auditable AI solutions.…I am Sunil Kumar Medara, a NVIDIA Certified GenAI Engineer with 6+ years of building production-grade ML, GenAI, and data engineering solutions across healthcare, nonprofit, financial services, and logistics. I specialize in LLM-powered applications, RAG pipelines, and agentic AI workflows, delivering scalable AWS architectures and MLOps practices that drive measurable business impact. I enjoy translating complex data problems into secure, reliable AI systems, leading cross-functional teams, and delivering dashboards that tell the story behind the numbers. My hands-on experience spans from model development to real-time inference and governance, ensuring robust, auditable AI solutions.

Sunil Kumar Medara

Data Scientist, AI Engineer, AI Developer, +2

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I am Sunil Kumar Medara, a NVIDIA Certified GenAI Engineer with 6+ years of building production-grade ML, GenAI, and data engineering solutions across healthcare, nonprofit, financial services, and logistics. I specialize in LLM-powered applications, RAG pipelines, and agentic AI workflows, delivering scalable AWS architectures and MLOps practices that drive measurable business impact. I enjoy translating complex data problems into secure, reliable AI systems, leading cross-functional teams, and delivering dashboards that tell the story behind the numbers. My hands-on experience spans from model development to real-time inference and governance, ensuring robust, auditable AI solutions.…I am Sunil Kumar Medara, a NVIDIA Certified GenAI Engineer with 6+ years of building production-grade ML, GenAI, and data engineering solutions across healthcare, nonprofit, financial services, and logistics. I specialize in LLM-powered applications, RAG pipelines, and agentic AI workflows, delivering scalable AWS architectures and MLOps practices that drive measurable business impact. I enjoy translating complex data problems into secure, reliable AI systems, leading cross-functional teams, and delivering dashboards that tell the story behind the numbers. My hands-on experience spans from model development to real-time inference and governance, ensuring robust, auditable AI solutions.

Available to hire

I am Sunil Kumar Medara, a NVIDIA Certified GenAI Engineer with 6+ years of building production-grade ML, GenAI, and data engineering solutions across healthcare, nonprofit, financial services, and logistics. I specialize in LLM-powered applications, RAG pipelines, and agentic AI workflows, delivering scalable AWS architectures and MLOps practices that drive measurable business impact.

I enjoy translating complex data problems into secure, reliable AI systems, leading cross-functional teams, and delivering dashboards that tell the story behind the numbers. My hands-on experience spans from model development to real-time inference and governance, ensuring robust, auditable AI solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Gen AI Engineer at American Diabetes Association

December 1, 2024 - Present

Designed and deployed a production-grade enterprise RAG system using AWS Bedrock, Amazon Kendra, and FAISS/Pinecone for secure, citation-backed semantic search across donor and operational datasets. Architected the full RAG pipeline, including document ingestion, adaptive chunking, embedding generation, vector indexing, and metadata-aware retrieval to reduce hallucination risk. Built a hybrid retrieval architecture combining dense vector search, keyword search, metadata filtering, and reranking for mixed structured/unstructured data sources. Developed agentic AI workflows using Bedrock Agents and LangChain orchestration to autonomously analyze anomalies, retrieve pipeline context, and generate executive-ready root-cause summaries. Established LLMOps monitoring with MLflow covering regression testing, retrieval benchmarking, latency profiling, and cost-per-query analysis. Designed scalable distributed architecture with SQS, Lambda/ECS, DynamoDB, and S3 for resilient data ingestion and r

Gen AI Engineer at Community Associations Institute (CAI)

May 1, 2024 - November 1, 2024

Built an AI-powered knowledge search experience on Amazon Kendra enabling operations teams to query supply-chain documents and SOPs in natural language with source-linked results. Improved retrieval quality through standardized document parsing, consistent metadata tagging, and deduplication of noisy content. Added LLM-style summarization and Q&A on top of search results with confidence thresholds, output formatting, and safe handling for out-of-scope queries. Instrumented usage and quality tracking and iterated on metadata structure to improve answer usefulness over time. Built and automated ETL/ELT pipelines processing 1M+ records using Python and SQL, enforcing validation rules and keeping reporting datasets consistent. Designed AWS Redshift reporting marts and tuned complex SQL for improved dashboard refresh performance. Developed interactive Tableau dashboards and anomaly detection models (Random Forest, SVM) to flag unusual supply-chain patterns for leadership.

Gen AI Engineer at University of Maryland

May 1, 2023 - April 1, 2024

Built an 'ask-and-explain' analytics workflow enabling institutional users to query KPI trends in plain language and receive structured, context-aware summaries linked to underlying data sources. Developed LLM-style report generation patterns converting raw metrics into executive narratives (what changed, why it matters, what to check), significantly reducing manual effort on recurring leadership updates. Built time-series forecasting models (ARIMA/Prophet) and interactive 'what-if' scenario views for strategic enrollment planning and operational decision-making processes. Automated recurring reporting pipelines using Python, improving data consistency while reducing turnaround time and minimizing human error in weekly/monthly reporting cycles. Standardized KPI definitions in reusable SQL views and Power BI/Tableau dashboards with built-in anomaly detection, trend tracking, and performance outlier identification mechanisms.

Data Scientist at Deloitte USI

November 1, 2020 - January 1, 2023

Built anomaly detection and classification models with explainability (SHAP) so results were trusted and acted upon by stakeholders. Developed near-real-time analytics by ingesting high-velocity events via Apache Kafka and Spark Streaming, eliminating batch-cycle delays for downstream teams. Maintained ETL pipelines using AWS Glue, DMS, and Lambda, improving dataset freshness and reporting reliability. Engineered behavioral and operational ML features that measurably improved model performance. Designed dimensional reporting datasets enabling BI teams to build dashboards; built Power BI dashboards for logistics KPI monitoring.

Junior Data Scientist at Maxgen Technologies Pvt. Ltd.

October 1, 2019 - October 1, 2020

Assisted in building forecasting, classification, and customer segmentation models using Python and R, improving accuracy through feature engineering and model tuning. Delivered end-to-end data preparation for 100K+ record datasets using optimized Pandas/NumPy pipelines. Created reusable transformation scripts and anomaly checks to ensure data quality and accelerate future project delivery.