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I am a Generative AI Engineer and Data Scientist with 9+ years of hands-on experience in ML, NLP, and large language model-based application development across healthcare, finance, and enterprise AI domains. I design, fine-tune, and deploy LLMs such as GPT, LLaMA, Falcon, and Mistral, build Retrieval-Augmented Generation pipelines, and engineer end-to-end AI solutions that automate clinical, underwriting, and customer-service workflows. I lead cross-functional teams, implement responsible AI practices, optimize inference latency, and deliver scalable production systems using Python, PyTorch/TensorFlow, LangChain, vector databases, and MLOps tooling on AWS, GCP, and Azure. I focus on business impact, governance, and privacy, ensuring compliant, auditable AI solutions.…I am a Generative AI Engineer and Data Scientist with 9+ years of hands-on experience in ML, NLP, and large language model-based application development across healthcare, finance, and enterprise AI domains. I design, fine-tune, and deploy LLMs such as GPT, LLaMA, Falcon, and Mistral, build Retrieval-Augmented Generation pipelines, and engineer end-to-end AI solutions that automate clinical, underwriting, and customer-service workflows. I lead cross-functional teams, implement responsible AI practices, optimize inference latency, and deliver scalable production systems using Python, PyTorch/TensorFlow, LangChain, vector databases, and MLOps tooling on AWS, GCP, and Azure. I focus on business impact, governance, and privacy, ensuring compliant, auditable AI solutions.

Dinesh Reddy

Data Scientist, AI Engineer, Full Stack Developer, +3

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I am a Generative AI Engineer and Data Scientist with 9+ years of hands-on experience in ML, NLP, and large language model-based application development across healthcare, finance, and enterprise AI domains. I design, fine-tune, and deploy LLMs such as GPT, LLaMA, Falcon, and Mistral, build Retrieval-Augmented Generation pipelines, and engineer end-to-end AI solutions that automate clinical, underwriting, and customer-service workflows. I lead cross-functional teams, implement responsible AI practices, optimize inference latency, and deliver scalable production systems using Python, PyTorch/TensorFlow, LangChain, vector databases, and MLOps tooling on AWS, GCP, and Azure. I focus on business impact, governance, and privacy, ensuring compliant, auditable AI solutions.…I am a Generative AI Engineer and Data Scientist with 9+ years of hands-on experience in ML, NLP, and large language model-based application development across healthcare, finance, and enterprise AI domains. I design, fine-tune, and deploy LLMs such as GPT, LLaMA, Falcon, and Mistral, build Retrieval-Augmented Generation pipelines, and engineer end-to-end AI solutions that automate clinical, underwriting, and customer-service workflows. I lead cross-functional teams, implement responsible AI practices, optimize inference latency, and deliver scalable production systems using Python, PyTorch/TensorFlow, LangChain, vector databases, and MLOps tooling on AWS, GCP, and Azure. I focus on business impact, governance, and privacy, ensuring compliant, auditable AI solutions.

Available to hire

I am a Generative AI Engineer and Data Scientist with 9+ years of hands-on experience in ML, NLP, and large language model-based application development across healthcare, finance, and enterprise AI domains. I design, fine-tune, and deploy LLMs such as GPT, LLaMA, Falcon, and Mistral, build Retrieval-Augmented Generation pipelines, and engineer end-to-end AI solutions that automate clinical, underwriting, and customer-service workflows.

I lead cross-functional teams, implement responsible AI practices, optimize inference latency, and deliver scalable production systems using Python, PyTorch/TensorFlow, LangChain, vector databases, and MLOps tooling on AWS, GCP, and Azure. I focus on business impact, governance, and privacy, ensuring compliant, auditable AI solutions.

Skills

Experience Level

Expert

Expert

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Intermediate

Language

English

Fluent

Work Experience

Gen AI Engineer at Mayo Clinic

April 1, 2024 - Present

Led GenAI initiatives to automate clinical documentation, radiology summarization, and provider workflow support. Reduced documentation turnaround by 10% via automated summaries integrated into EHR-aligned pipelines. Adapted GPT-family and domain-specific LLMs (ClinicalBERT, LLaMA derivatives) with retrieval-augmented techniques for clinical text understanding. Engineered secure, in-VPC RAG pipelines using LangChain, LlamaIndex, ChromaDB, and FAISS; integrated Bedrock endpoints for PHI-safe experimentation. Built clinical assistants for note drafting and information lookup using FastAPI, Django, and React UI. Prototyped guarded agentic workflows (CoT/ReAct) for extracting structured insights from radiology reports; developed model-driven microservices to automate triage and data extraction; deployed CNN/ViT imaging models with TensorRT optimization on AWS. Evaluated medical-domain LLMs on GCP Vertex AI and AWS Bedrock; created SageMaker-based experimentation pipelines; containerized se

Gen AI/Data Scientist at MetLife Insurance

October 1, 2022 - March 1, 2024

Designed and deployed GenAI solutions for claims adjudication, policy summarization, and customer-support automation; integrated secure domain retrieval (Azure OpenAI, private FAISS/Qdrant) to align retrieval with policy documents and claims notes. Achieved ~15% faster claims turnaround via automated summarization and intent-classification pipelines. Built end-to-end AI pipelines on Azure ML and Databricks (Medallion architecture) with bronze–silver–gold data layers and model monitoring. Implemented MLOps with MLflow, Airflow, Docker, and Kubernetes to ensure governance and reproducibility. Used GitHub Copilot to accelerate Python development for LLM workflows and unit-test templates. Prototyped agentic reasoning (ReAct/CoT) for structured extraction from long-form narratives; developed self-hosted Qdrant-based retrieval for multimodal documents; applied OCR, NLP, and semantic search in ETL flows to detect inconsistencies and automate eligibility checks. Built Streamlit and Power B

AI/ML Engineer at Tesla

August 1, 2020 - September 1, 2022

Contributed to camera-based autonomous driving stack by developing perception models and dataset pipelines; improved object detection, lane understanding, and trajectory prediction reliability across diverse conditions. Enhanced multi-camera perception for temporal consistency and reduced false positives; designed CNN/ViT architectures for lane boundaries, object detection, and segmentation using hard-negative mining. Processed fleet video datasets with Apache Spark and Airflow; built embedded-efficient CNNs for FSD onboard compute to lower per-frame latency. Integrated updated networks into Autopilot builds and validated behaviors in simulation and fleet logs. Collaborated with perception, planning, data, and systems teams; leveraged in-house HPC clusters for large-scale training; deployed inference on Tesla GPUs; evaluated models with shadow mode in simulation and real-world logs.

Data Scientist/ML Engineer at Mastercard

September 1, 2018 - July 1, 2020

Improved fraud-detection precision by 12% and reduced false positives by 9% using supervised ML on transaction-behavior features. Built real-time scoring pipelines with Kafka and Spark Streaming delivering sub-150 ms latency at scale. Developed scalable feature engineering and retraining workflows with MLflow, Databricks, and Docker-based services; implemented Terraform/CloudFormation for CI/CD and secure deployment on PCI-aligned infra. Created anomaly detection and clustering models (k-means, DBSCAN) for merchant and spend-pattern profiling; delivered executive dashboards in Power BI/Tableau for monitoring. Implemented drift detection, threshold monitoring, and data-quality checks to maintain robust decision quality.

Python Developer/Data Scientist at Aditya Birla Fashion & Retail Ltd.

April 1, 2016 - May 1, 2018

Built large-scale Hadoop pipelines (HDFS, MapReduce, Hive, Pig) and Spark/PySpark ETL to process millions of daily POS transactions for enterprise retail analytics. Implemented real-time ingestion via Kafka + Flume; developed SKU-demand forecasting models (ARIMA/SARIMA, XGBoost) and customer segmentation (k-means, DBSCAN) to optimize pricing, inventory, and promotions. Built recommendation modules (Spark MLlib) to boost basket value; developed Flask-based REST APIs to serve forecasts, segments, and KPIs; containerized services with Docker and deployed on AWS with CI/CD and secure IAM policies. Collaborated with merchandising teams to implement data pipelines compliant with governance and privacy standards.