AI/ML Engineer with 7+ Years Experience

AI/ML Engineer

Hariprasad Chintakindi

+1-703-***-**** *********************@*****.***

LinkedIn ID : https://www.linkedin.com/in/hariprasad-chintakindi-3133501a1/ Professional Summary

AI/ML Engineer with 7+ years of experience designing, deploying, and operationalizing machine learning and Generative AI solutions across healthcare and banking. Strong expertise in Python, NLP, LLMs, RAG architectures, and cloud platforms (AWS, Azure, GCP) with hands-on MLOps, Kubernetes, and CI/CD automation. Proven track record of building scalable chatbots, predictive models, and data pipelines with a focus on performance, security, and compliance. Adept at collaborating with cross-functional teams to deliver production-grade AI solutions that drive business impact. TECHNICAL SKILLS

• Programming & Scripting Languages: Python, SQL, R, Java, C++

• Gen AI & LLM Technologies: GPT-4/3.5, Claude, Gemini, Mistral-7B, Azure OpenAI, OpenAI APIs, Llama Models, Transformer Architectures, Prompt Engineering, Context Caching, Memory Optimization, LLM Evaluation (toxicity, bias, hallucination), Re- Ranking Pipelines, Hybrid Retrieval, Semantic Search

• Agentic AI & RAG Systems: LangChain, LangGraph, AutoGen, CrewAI, Retrieval-Augmented Generation (RAG), Embedding Pipelines (SentenceTransformers, OpenAI), Vector Databases (FAISS, Qdrant, Pinecone), Query Rewriting, Document Chunking, Retrieval Optimization

• Machine Learning, Deep Learning & Analysis Techniques: Logistic regression, KNN, Gradient Boosting, XGBoost, Linear Regression, Decision Trees, Random Forest, K-means Clustering, Support Vector Machines, NLP, LSTM, Time series analysis and Forecasting, Customer segmentation, Naïve Bayes, Gaussian Mixture Models, ANOVA, ARIMA, SMOTE, Prophet, Classification and Regression Trees (CART), Ensemble Learning (Bagging Boosting), Random Forests, Principal Component Analysis (PCA), Auto Encoders, Deep Learning

• Libraries & Frameworks: NumPy, Pandas, Scikit-Learn, TensorFlow, Matplotlib, Seaborn, PySpark, PyTorch, Scipy, NLTK, SQL Alchemy, PyMongo, Keras, OpenCV

• Cloud Services, Platforms & MLOps: Azure (Data Factory, Synapse Analytics, Data Lake Storage), AWS (EC2, Sage Maker, Aurora, Quick Sight), Cloud Platforms, MLOps

• Big Data: Hadoop 3.0, Spark 2.3, Hive 2.3, MapReduce

• Database Tools: Toad, SQL Developer, PL/SQL Developer, Informatica Power Center 9.5.1. PROFESSIONAL EXPERIENCE

Trustmark Bank July 2024 - Present

Role : AI/ML Engineer Austin, Texas

• Built and optimized conversational flows with advanced intent detection, context management, slot filling, and rich fulfillment.

• Leveraged state -of-the-art NLP, LLM, and GenAI models on Vertex AI for complex intent handling, summarization, and entity extraction.

• Built and maintained data ingestion and preprocessing pipelines on ADLS Gen2, Delta Lake, and Synapse for ML-ready datasets.

• Implemented model monitoring frameworks using Azure Monitor, Application Applications Insights, and custom telemetry for drift detection, performance tracking, and automated retraining triggers.

• Developed and maintained cloud-native chat solutions using GCP services for real-time analytics and reporting.

• Experienced working on prompt engineering for generative AI using Hugging Face Transformers and LangChain for data synthesis, image and video augmentation.

• Designed, build, and implemented classification models like random forest classifier, Artificial Neural network classifier, Multiclass boosted decision tree.

• Using machine learning techniques supervised, unsupervised, reinforcement learning and understand the requirement & design for AI/ML use cases.

• Utilized Google Cloud Platform big data tools including Big Query and Dataproc, to streamline data lakes and leveraged Google AutoML for automating the model building process.

• Perform unit testing and provide system test support and Valid date & monitor deliverable in production.

• Employed Generative Adversarial Networks (GANs) to produce synthetic data, thereby expanding limited datasets for training machine learning models and improving model robustness.

• Managed feature pipelines using Databricks Feature Store and Azure ML Feature Store, ensuring consistent features across training and serving.

• Deployed ML models using Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and Azure ML Endpoints for secure, scalable inference.

• Integrated MLflow Tracking and Registry for experiment management, artifact versioning, and governed model promotion workflows.

• Used R for prototype on a sample data exploration to identify the best algorithmic approach and then wrote scala scripts using spark machine learning module.

• Designed and implemented Large Language Models (LLMs) to provide real-time financial advisory chatbots, utilizing BERT, GPT-3/4, Lang Chain, and retrieval-augmented generation (RAG) for investment insights.

• Used PyTorch and TensorFlow to build Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for financial pattern recognition and market forecasting.

• Led MLOPs automation for chatbot pipelines, including training, deployment, monitoring, and versioning with Docker, Kubernetes, and CI/CD workflows.

• Integrated chatbots with CRM systems and live agent platforms, ensuring secure and complaint data handling.

• Collaborated in Agile teams using Scrum, Kanban, and tools like Confluence for project management and documentation.

• Designed, built, and trained ML- Artificial intelligence models such as Linear Regression, Decision Tree, Random Forest Regressor, Boosted Decision Trees regressor, Cat Boost, Light, XGBoost Regressor, Time series forecasting - ARIMA, ARIMAX.

• Mentored junior engineers and led cross-functional teams to deliver end-to-end AI projects. Mayo Clinic July 2023 - June 2024

Role : AI/ ML Engineer Austin, Texas

• Implemented MLOps frameworks using SageMaker, CodePipeline, CodeBuild, and GitHub Actions for automated model packaging, testing, and promotion to production

• Designed and deployed NLP-driven Chatbots using Dialogflow ES and custom transformer models.

• Built scalable data processing workflows using AWS Glue, AWS Lambda, Athena, EMR (PySpark) and integrated them with ML pipelines.

• Implemented RESTful APIs and gRPC for seamless integration with enterprise systems..

• Built feature engineering and data transformation workflows using Glue Jobs, Glue Crawlers, and AWS Step Functions for orchestrated ML- ready datasets.

• Implemented role-based access control, VPC-secured endpoints, encryption, and secret management using IAM, KMS, and Secrets Manager.

• Documented model lifecycle workflows, data flows, monitoring procedures, and operational guidelines for audit readiness and compliance.

• Collaborated with cross-functional teams across cloud platforms (AWS, GCP, Azure) to integrate ML models into enterprise applica-tions, analytics dashboards, and APIs, ensuring seamless production delivery.

• Led a team to integrate Edge AI systems using PyTorch Mobile at client sites, reducing latency and improving real-time transaction monitoring in high-frequency trading environments.

• Designed CNNs for document classification tasks using OpenCV, improving the accuracy and speed of processing compliance documents.

• Leveraged AWS Lambda for serverless computing tasks and deployed machine learning models at scale using Google Cloud Vertex AI, effectively handling billions of daily ad requests.

• Updated Python scripts to match training data with our database stored in AWS Cloud Search, so that we would be able to assign each document a response label for further classification.

• Collaborated with cross-functional teams to integrate XAI methods (Explainable AI) into production systems, enabling more trans- parent and trustworthy AI-driven decision-making processes.

• Developed scripts in Python using the Scikit-learn, spaCy, Transformers, data science and TensorFlow machine learning libraries.

• Implemented automated model drift detection, performance monitoring, and retraining workflows using CloudWatch, SageMaker Model Monitor, and lambda-triggered automation.

• Developed containerized ML workloads using Docker and deployed them through SageMaker, ECR, and ECS/EKS for scalable inference.

• Performed exploratory data analysis, feature engineering, and model development using Python, pandas, NumPy, scikit-learn, Tensor- Flow, PyTorch, and SageMaker notebooks.

• Designed LLM-based recommendation systems using GPT, LangChain, and RAG to enhance personalized ad targeting for advertisers.

• Fine-tuned LLMs and developed RAG pipelines for enhanced conversational intelligence.

• Participated in features engineering such as feature generating, PCA, feature normalization and label encoding with Scikit-learn pre- processing.

• Used Python 2.x/3.X (NumPy, SciPy, Pandas, Scikit-learn, Seaborn to develop a variety of models and algorithms for analytic purposes.

• Developed a Machine Learning test-bed with 24 different model learning and feature learning algorithms.

• Used Pandas, NumPy, Seaborn, SciPy, Matplotlib, Sci-kit-learn, and NLTK in Python for developing various machine learning algorithms.

Baylor Scott & White Health June 2020 - July 2022

Role : Machine Learning Engineer India

• Developed and deployed machine learning models for classification, forecasting, recommendation, and predictive analytics use cases across large-scale datasets.

• Designed and implemented end-to-end ML pipelines using Python, PySpark, and distributed computing frameworks to automate feature engineering, training, validation, and inference.

• Built scalable data processing workflows leveraging ETL frameworks, SQL, and big data platforms (Hadoop/Spark) to prepare ML- ready datasets.

• Operationalized models into production using APIs, microservices, and containerized deployments with Docker and Kubernetes.

• Performed exploratory data analysis (EDA), feature selection, model tuning, and performance optimization using scikit-learn, Tensor- Flow, or PyTorch.

• Integrated automated model monitoring and alerting for drift detection, performance degradation, and retraining triggers.

• Created reusable templates, shared libraries, and standardized ML development practices to accelerate experimentation and promote consistency.

• Collaborated with data scientists, data engineers, and product teams to translate business requirements into scalable ML solutions.

• Developed various machine learning models such as Logistic regression, KNN, and Gradient Boosting with Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn in Python.

• Worked on various machine learning algorithms like Linear regression, logistic regression, Decision trees, random forests, K-means clustering, Support vector machines, XG Boosting on client requirements.

• Worked on AWS boto3 API to make the HTTP calls to AWS amazon web services like S3, AWS secrets manager, AWS SQS.

• Analyzed large data sets apply machine learning techniques and develop predictive models, statistical models and developing and enhancing statistical models by leveraging best-in-class modeling techniques.

• Partnered with merchandising and marketing teams to translate analytical insights into targeted promotions and inventory optimization strategies.

• Applied time series forecasting (ARIMA, Prophet, LSTM) to improve accuracy of sales and inventory predictions across multiple product categories.

• Visualized actionable insights using Tableau and Power BI, enabling leadership to make data-driven business decisions.

• Worked closely with data engineers and cloud architects to productionize ML models using AWS SageMaker and Lambda for scalable real- time inference.

• Presented findings and model outcomes to executive stakeholders, influencing pricing, assortment, and loyalty program initiatives.

• Worked on natural language processing for documentation classification, text processing using NLTK, SPACY, Text Blob to find the sensitive information in the electronically stored files and text summarization. Artificial penetration software Solutions Private Limited June 2018 - May 2020 Role : Data Scientist / ML India

• Built time-series forecasting models to enable accurate demand prediction and inventory optimization for supply chain operations.

• 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 leveraging collaborative filtering and deep learning techniques for personalized user experiences.

• Implemented MLOps pipelines using MLflow, Docker, Kubernetes, and Airflow for seamless model deployment and lifecycle management.

• Automated data preprocessing and feature engineering workflows using PySpark, Pandas, and NumPy to handle large-scale structured and unstructured data.

• Designed real-time data streaming pipelines with Kafka and Spark Streaming for continuous data ingestion and analytics processing.

• Integrated data pipelines with Databricks and Delta Lake for high-volume data ingestion, transformation, and analysis in distributed environments.

• Conducted predictive maintenance modeling using IoT sensor data and ML algorithms to reduce operational downtime in manufacturing systems.

• Conducted A/B testing and statistical experiments to evaluate the impact of new product features and marketing campaigns on user engagement.

• Created interactive data visualization dashboards using Power BI, Tableau, and Plotly for actionable insights and executive reporting.

• Applied unsupervised learning techniques like clustering (K-Means, DBSCAN) and dimensionality reduction (PCA, t-SNE) for customer segmentation analysis.

• Deployed computer vision models using OpenCV and PyTorch for object detection, image recognition, and OCR applications.

• Developed feature selection and optimization pipelines using SHAP, LIME, and mutual information methods for interpretable ML solutions.

• Integrated cloud services like AWS SageMaker, GCP Vertex AI, and Azure ML for scalable model training and deployment. Academics:

Master of Science in Data Analytics

Concordia University St.paul, Minneapolis, MN - December -2023. GPA 3.6

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