Available to hire
Hi, I’m Hariprasad Chintakindi, an AI/ML Engineer with 7+ years designing, deploying, and operationalizing machine learning and Generative AI solutions across healthcare and banking. I specialize in Python, NLP, LLMs, RAG architectures, and cloud platforms (AWS, Azure, GCP) with hands-on MLOps, Kubernetes, and CI/CD automation. I enjoy turning complex data problems into production-grade AI solutions that drive business impact while ensuring security and compliance.
I’m passionate about building scalable chatbots, predictive models, and data pipelines, collaborating with cross-functional teams to deliver practical AI that informs decision-making and improves user experiences.
Skills
Language
English
Fluent
Work Experience
AI/ML Engineer at Trustmark Bank
July 1, 2024 - PresentBuilt and optimized conversational flows with advanced intent detection, context management, slot filling, and rich fulfillment. Leveraged NLP, LLMs, and Generative AI models on Vertex AI for complex intent handling, summarization, and entity extraction. Designed and maintained data ingestion and preprocessing pipelines on ADLS Gen2, Delta Lake, and Synapse to create ML-ready datasets. Implemented model monitoring frameworks using Azure Monitor, Application Insights, and custom telemetry to detect drift, track performance, and trigger automated retraining. Developed and deployed cloud-native chat solutions for real-time analytics and reporting, integrating with CRM systems and live agent platforms while ensuring secure data handling. Experience with prompt engineering for generative AI, data synthesis, and retrieval-augmented generation (RAG) using LangChain and Hugging Face Transformers. Led MLOps automation for chatbot pipelines including training, deployment, monitoring, and version
AI/ML Engineer at Mayo Clinic
July 1, 2023 - June 1, 2024Implemented MLOps frameworks using SageMaker, CodePipeline, CodeBuild, and GitHub Actions to automate model packaging, testing, and production promotion. Designed and deployed NLP-driven chatbots with Dialogflow ES and custom transformer models. Built scalable data processing workflows using AWS Glue, Lambda, Athena, and EMR (PySpark), and integrated feature engineering with Glue Jobs/Crawlers and Step Functions for ML-ready datasets. Implemented RESTful APIs and gRPC for enterprise integration. Orchestrated ML-ready datasets with Glue Jobs/Crawlers and Step Functions. Enforced security best practices (IAM, KMS, Secrets Manager) and documented model lifecycle for audit readiness. Collaborated across cloud platforms to deliver production-grade AI solutions. Led Edge AI integration using PyTorch Mobile for client sites to reduce latency in real-time decision tasks. Fine-tuned LLMs and implemented RAG pipelines to enhance conversational intelligence for enterprise users.
Machine Learning Engineer at Baylor Scott & White Health
June 1, 2020 - July 1, 2022Developed and deployed machine learning models for classification, forecasting, and predictive analytics on large-scale datasets. Built end-to-end ML pipelines with Python, PySpark, and distributed computing frameworks to automate feature engineering, training, validation, and inference. Operationalized models into production via APIs, microservices, and containerized deployments using Docker and Kubernetes. Implemented automated monitoring with drift detection and performance dashboards, and collaborated with data engineers to productionize models with AWS SageMaker and Lambda for scalable real-time inference. Applied time series forecasting (ARIMA, Prophet, LSTM) to sales and inventory data, and leveraged NLP for document processing tasks. Created reusable templates and standardized ML development practices to accelerate experimentation and ensure governance.
Data Scientist / ML at Artificial Penetration Software Solutions Private Limited
June 1, 2018 - May 1, 2020Built time-series forecasting models for supply chain demand and inventory optimization; developed NLP pipelines for text classification, sentiment analysis, and topic modeling using Hugging Face Transformers, spaCy, and BERT-based models. Created AI-powered recommendation engines with collaborative filtering and deep learning. Implemented MLOps pipelines with MLflow, Docker, Kubernetes, and Airflow for end-to-end model deployment and lifecycle management. Automated data preprocessing and feature engineering with PySpark, Pandas, and NumPy; designed real-time data streaming pipelines with Kafka and Spark Streaming; integrated with Databricks and Delta Lake for scalable analytics. Conducted predictive maintenance modeling on IoT data; performed AB testing and statistical experiments to validate features; built dashboards with Power BI/Tableau for executive reporting.
Education
Master of Science in Data Analytics at Concordia University, St. Paul, MN
January 11, 2030 - December 1, 2023Qualifications
Industry Experience
Healthcare, Financial Services, Software & Internet, Education, Professional Services
Skills
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