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Aravind B - Gen AI/AI/ML Developer

[email protected]

Location: Tampa, Florida, USA

Relocation: yes

Visa: H1B

Resume file: Aravind_AIML_Engineer-10_1745415394763.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

Professional Summary: AI/ML Engineer with 10 years of expertise in Data Science, Machine Learning, Deep Learning, and Large Language Models (LLMs), specializing in building, fine-tuning, and deploying scalable AI-driven solutions. Expertise in developing, fine-tuning, and deploying LLMs and Generative AI using GPT- 4, Gemini AI, LLaMA, Falcon, and BLOOM, with proficiency in diffusion models, GANs, VAEs, and parameter-efficient fine-tuning (LoRA, QLoRA, PEFT). Designed and deployed personalized treatment recommendation systems using reinforcement learning and patient history analysis to optimize medication plans and improve healthcare outcomes. Implemented Retrieval-Augmented Generation (RAG) using vector databases (FAISS, Pinecone, Weaviate, ChromaDB) for enterprise AI solutions; hands-on experience with Python, TensorFlow, PyTorch, and Hugging Face Transformers. Developed and optimized NLP models (e.g., BERT, GPT, T5, RoBERTa) with advanced tokenization techniques (Word2Vec, FastText, BPE, Sentence Transformers) for chatbots, document summarization, and text analytics using NLTK, SpaCy, and OpenAI APIs. Deployed AI/ML models on AWS, Azure, and GCP using SageMaker, Vertex AI, and Azure ML; skilled in serverless architectures (AWS Lambda, Cloud Run) and distributed model training with PySpark and Databricks. Built scalable AI-driven data pipelines using Apache Airflow, Kafka, and Snowflake, ensuring efficient data processing and real-time inference with integrated feature stores (Feast, Databricks Feature Store). Proficient in processing large-scale structured and unstructured data with Python, PySpark, Snowflake, BigQuery, and Redshift, enhancing real-time model inference and retraining workflows. Hands-on experience with containerized deployments using Docker, Kubernetes, and Helm, optimizing model serving with KServe, TensorFlow Serving, and Triton Inference Server. Utilized MLflow, Kubeflow, and TensorFlow Extended (TFX) for automated model versioning, tracking, and lifecycle management, ensuring continuous improvement and governance. Integrated AWS SageMaker Model Monitor, GCP Vertex AI, and Azure ML for model drift detection, performance evaluation, and anomaly detection, maintaining reliable AI performance. Built HIPAA- and FHIR-compliant AI/ML models for secure healthcare data processing and EHR integration, enhancing predictive analytics for patient outcomes. Proficient in API development using FastAPI, Flask, and Uvicorn, delivering asynchronous AI inference services and scalable API solutions for AI-powered applications. Skilled in evaluation metrics like precision, recall, F1-score, and AUC-ROC, ensuring 95%+ accuracy and 90% precision in classification and regression tasks. Skills: Programing & Scripting Languages Python, Pyspark, Java, SQL, Bash, Perl, YAML, Groovy Machine Learning Supervised Learning, Unsupervised Learning, Feature Engineering, Model Optimization, Time Series Analysis, Recommendation Systems, Sentiment Analysis, Evaluation Metrics. Deep Learning TensorFlow, PyTorch, Keras, CNNs, RNNs, LSTMs, GANs, Transformer Models, ANN, Transfer Learning, Ensemble Models, Computer Vision. Large Language Models (LLMs) GPT, BERT, LLaMA, Falcon, Hugging Face Transformers, LoRA, DeepSpeed. Retrieval-Augmented Generation (RAG) FAISS, ChromaDB, Pinecone, Weaviate, Llama Index. Generative AI Fine-tuning LLM s, Langchain, Llama index, RAG, AI Agents, CrewAI, Auto Gen, Prompt Engineering, Hugging face, Phi, OpenAI, Llama, FIASS. Big Data & Distributed Computing HDFS, MapReduce, PySpark Model Serving FastAPI, Flask, Streamlit, Kserve, TensorFlow Serving, TorchServe, Cloud Run, Vertex AI Endpoints, SageMaker Endpoints, Azure ML endpoints. Cloud Platforms AWS (SageMaker, Bedrock, Lambda, Lex, CloudWatch, CloudTrail, Redshift ML, DynamoDB, CodeBuild, CodeDeploy, S3, EC2, IAM, AMIs). GCP (Vertex AI, AutoML, Cloud Vision API, Dialogflow, NVIDIA GPUs, BigQuery ML, VM Instance, VPC). Azure (Azure ML, Azure AI & OpenAI, Blob Storage, Azure Functions, Azure Cognitive Services) MLOps & CI/CD GitHub Actions, GitLab CI/CD, Bitbucket Pipelines, Jenkins, CircleCI, Cloud Build, CodePipeline, Azure Pipelines, MLflow, Kubeflow, DVC, DagsHub Monitoring & Logging AWS CloudWatch, GCP Cloud Monitoring, Azure Monitor, CloudTrail Logs, Prometheus, Grafana, MLflow, Weights & Biases, TensorBoard, Evidently AI, Vertex AI Model Monitoring, Cloud Logging MLOps & Workflow Orchestration Kubeflow, MLflow, Airflow, TFX Pipelines, Vertex AI Pipelines, Supervised/Unsupervised Algorithms, ANN, CNN, NLP, Computer Vision, GAN s, LSTM, Feature Engineering, Transfer Learning, ensemble models, Time series, Recommendation systems, Sentiment Analysis, Evaluation Metrics. . Frame Works PySpark, Tensorflow, Keras, PyTorch, Scikit-Learn, openCV, NLTK, Pandas, Transformers, Flask, Celery, FastAPI, Streamlit, Gradio, Pickle, Pydantic, Anaconda, Jupyter Notebook. Version Control GIT, GitHub, Azure Repos, AWS Code Commit. Data Warehousing Snowflake, Data Lake Storage, Google Big Query. Container Orchestration and Infrastructure as Code Docker, Kubernetes, Terraform. Operating Systems Windows, Ubuntu, Linux. Certifications: Azure Certification - Link Professional Experience: Client: Motive MI, SFO, CA(Remote) Jun 2023 - Jan 2025 Role: Senior AI/ML GenAI Engineer Responsibilities: Developed Generative AI-powered chatbots using AWS Bedrock and fine-tuned LLMs (e.g., GPT-4, Falcon, T5) for real-time patient symptom analysis and personalized treatment recommendations. Implemented Retrieval-Augmented Generation (RAG) with FAISS and AWS OpenSearch to enhance chatbot accuracy by integrating real-time EHR and clinical trial data. Leveraged Deep Learning and NLP models (e.g., BERT, T5) with SpaCy for clinical text processing, enabling the chatbot to extract vital health indicators from unstructured medical documents. Designed and deployed LLM-powered chatbot APIs using TensorFlow Serving, PyTorch, FastAPI, and Google Vertex AI, ensuring low-latency inference and scalable deployment for real-time medical queries. Developed feature engineering pipelines using PySpark, Pandas, NumPy, SciPy, and scikit-learn for efficient data ingestion from AWS S3, Google Vertex AI, and Snowflake to optimize training data. Built secure, scalable, and compliant chatbot solutions with SageMaker endpoints, Google Vertex AI, Docker, and Kubernetes, ensuring HIPAA-compliant inferencing. Integrated AWS CloudWatch, Prometheus, and Grafana with Google Vertex AI for real- time chatbot monitoring, model drift detection, and automated retraining. Deployed fine-tuned LLMs on AWS SageMaker, AWS Bedrock, and Google Vertex AI for high-performance inference, powering the chatbot s natural language processing capabilities. Applied TF-IDF, BERT embeddings, and PCA for text feature extraction and dimensionality reduction, improving chatbot accuracy in understanding medical queries. Developed AI/ML models for secure healthcare data processing and predictive analytics using Python, TensorFlow, PyTorch, and NLP libraries, ensuring compliance with healthcare regulations like HIPAA and GDPR. Preprocessed medical images using OpenCV and wavelet transforms, enabling multi- modal capabilities in the chatbot to process and analyze medical visuals, all through Python. Implemented Named Entity Recognition (NER) using SpaCy and BioBERT in Python, enabling the chatbot to extract important medical entities such as symptoms and treatments from unstructured clinical texts. Fine-tuned GPT-4, LLaMA, and BioBERT models for clinical text analysis and AI-driven diagnosis within the chatbot, optimizing performance using LoRA, quantization, TensorRT with TensorFlow and PyTorch to reduce inference latency and improve conversational accuracy. Client: OPTUM, Albany, NY Dec 2021 May 2023 Role: AI/ML Engineer Responsibilities: Developed a predictive healthcare analytics system to assess patient readmission risks using Azure ML, Snowflake, and Python. Designed and implemented machine learning models (Logistic Regression, Random Forest, XGBoost, and LSTMs) using TensorFlow and PyTorch for time-series patient history analysis. Processed and cleaned structured & unstructured medical data from Azure Data Lake & Snowflake, handling missing values, feature engineering, and text-to-numeric transformations. Built an NLP pipeline using TensorFlow and PyTorch for analyzing doctor s notes and extracting clinical insights with Named Entity Recognition (NER). Deployed ML models as RESTful APIs using FastAPI and TensorFlow Serving, enabling real-time predictions for hospitals and medical staff. Optimized model inference performance with ONNX and TensorRT, reducing latency by 40% for real-time decision-making. Implemented dimensionality reduction techniques (PCA, t-SNE) to enhance feature selection and improve model efficiency on large-scale patient datasets. Integrated real-time monitoring with Azure Monitor, Prometheus, and MLflow, tracking model drift and performance metrics in production. Ensured data security and compliance by implementing RBAC (Role-Based Access Control), HIPAA, and GDPR standards with Azure Key Vault for encrypted storage. Developed a self-learning pipeline for continuous model retraining using new patient data, automating updates through Azure ML Pipelines and Databricks Notebooks. Designed and maintained an end-to-end CI/CD pipeline for ML model deployment using GitHub Actions, Docker, and Kubernetes in Azure Kubernetes Service (AKS). Collaborated with cross-functional teams including data scientists, cloud engineers, and healthcare professionals to improve patient risk prediction accuracy by 30%. Client: British American Tobacco, Albany, NY Mar 2020 - Nov 2021 Role: ML Engineer Responsibilities: Designed and implemented a predictive analytics solution using Azure Machine Learning and Databricks to forecast both cost changes and future sales volumes of tobacco products. Built ML models using Linear Regression, Gradient Boosting, SVM, ElasticNet, Random Forest, and K-Means clustering to analyze cost trends, demand patterns, and optimal pricing strategies. Developed large-scale data pipelines in PySpark within Databricks Notebooks to process past 3 years of historical sales data from Snowflake and predict sales amounts and costs for the next 2 years. Implemented feature engineering and data transformation techniques to extract meaningful insights from sales data, customer trends, and external market factors. Integrated Snowflake as a scalable data source for both batch and real-time ingestion, enabling automated data pipelines using Azure Data Factory. Trained and optimized ML models to minimize forecasting errors and improve cost efficiency and sales planning accuracy by leveraging Azure ML AutoML and Hyperparameter tuning. Deployed predictive models as endpoints in Azure ML and exposed them via FastAPI and Azure Functions for real-time inference and integration with business applications. Implemented CI/CD pipelines with Azure DevOps, GitHub Actions, and MLflow to automate model retraining, deployment, and versioning for continuous improvement. Monitored model performance, accuracy, and drift using Azure Monitor, MLflow, Prometheus, and Grafana, ensuring reliability in sales and cost predictions. Developed dashboards and visualizations in Power BI to provide stakeholders with insights into future sales forecasts, pricing strategies, and demand planning. Reduced forecasting errors by 25% and improved sales planning efficiency, enabling optimized production and pricing decisions for British American Tobacco. Maveric Systems, Chennai, India Apr 2018 - Feb 2020 Role: Data Scientist & DevOps Engineer Responsibilities: Developed predictive models using Python (pandas, NumPy, SciPy, scikit-learn) for credit risk and fraud detection. Designed feature engineering pipelines, handling missing values, outliers, and categorical encoding, improving model accuracy by XX%. Designed and implemented end-to-end machine learning pipelines using PySpark and Databricks Notebooks for sales forecasting. Built time-series forecasting models (ARIMA, Holt-Winters, Exponential Smoothing) to predict customer default risks. Developed ETL workflows using AWS Glue and PySpark, processing structured and semi- structured data. Deployed trained models as Flask APIs on AWS EC2, integrating them into real-time risk analysis systems. Automated CI/CD pipelines with Jenkins, AWS CodePipeline, and Docker, ensuring seamless model deployment. Configured AWS S3 for data storage with lifecycle rules for cost optimization. Monitored and logged model performance using AWS CloudWatch and Prometheus. Integrated models into business intelligence tools like Tableau and Power BI for reporting and analytics. Client Name: ACE Technologies, Hyderabad, India Jul 2015 - Mar 2018 Role: DevOps Engineer (Data Scientist) Responsibilities: Conducted customer segmentation using K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA). Developed Python-based data pipelines using pandas, NumPy, and PySpark, processing large financial datasets for risk modeling. Designed Python-based data preprocessing modules, handling missing values, feature scaling, and categorical encoding. Built demand forecasting models using Linear Regression, Decision Trees, and Random Forest with Python. Created ETL pipelines with Azure Data Factory, automating data ingestion and transformation from multiple sources. Managed data in Azure SQL Database and Azure Blob Storage, optimizing performance with indexing and partitioning. Deployed REST APIs with FastAPI and Flask on Azure App Services for real-time analytics. Automated model retraining and deployment using Azure DevOps pipelines, reducing deployment time by XX%. Set up monitoring and logging with Azure Monitor and Application Insights, tracking API usage and errors. Developed interactive Power BI dashboards, providing stakeholders with insights on customer behavior and sales trends. Secured data access with Azure RBAC, Managed Identities, and encryption, ensuring compliance with data security policies. Education: Bachelors in Computer Science JNTUK(2011-2015) Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree California Michigan New York