Indianapolis, Indiana, United States
o Experience in AI/ML Model Development, Generative AI, and Classical Machine Learning, leveraging frameworks like TensorFlow, PyTorch, and Hugging Face to build and deploy intelligent solutions. Skilled in Fine-tuning Large Language Models (LLMs) and optimizing models for performance, scalability, and efficiency. o Proficient in Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG) Framework, and Deep Learning techniques to enhance AI-driven applications. Expertise in LangChain for developing intelligent, context-aware systems and implementing AI Model Evaluation for continuous improvement. o Hands-on expertise in Model Deployment, Software Architecture, and MLOps, ensuring seamless integration of AI systems into production. Adept at Data Pipelines, Feature Engineering, Hyperparameter Tuning, and utilizing AI Frameworks & Libraries to drive innovation. o Extensive experience in building web applications using Django, Flask, and FastAPI, with expertise in designing and implementing scalable backend architectures. Proficient in managing database interactions using Django ORM and SQLAlchemy, ensuring efficient data handling. Hands-on experience with PostgreSQL, MySQL, and MongoDB, managing both relational and NoSQL database solutions. o Skilled in handling asynchronous task processing using RabbitMQ, Apache Kafka, and Redis Queue (RQ) for efficient message queuing. Experienced in RESTful API development and integrating GraphQL and gRPC to optimize inter-service communication and data exchange. o Expertise in Docker for containerizing applications and ensuring consistent environments. Hands-on experience with Kubernetes and OpenShift for managing microservices architecture, automating scaling, and orchestrating deployments. o Strong background in cloud platforms including AWS (EC2, Lambda, S3, RDS, DynamoDB), Microsoft Azure (App Services, Functions, SQL Database, Blob Storage), and Google Cloud Platform (Compute Engine, Cloud Functions, Cloud SQL, Cloud Storage) for deploying and managing applications at scale.
o Designed and implemented AI/ML Model Development workflows to build scalable and high-performance predictive models. Integrated JavaScript with RESTful APIs for dynamic AI-driven content updates. Developed and maintained AI-enabled web applications using the Django framework for rapid development. Designed and implemented MongoDB data models optimized for machine learning pipelines and scalable AI workloads. Optimized AI/ML Model Development by implementing efficient training pipelines using PyTorch, TensorFlow, and Scikit-learn. o Integrated TypeScript with GraphQL APIs to ensure strongly typed queries and mutations. Developed cutting-edge AI/ML Model Development strategies to improve business intelligence and analytics. Designed and implemented Generative AI models using GANs, VAEs, and diffusion models for image and text generation. Developed automated test cases using Pytest and Unittest for React and backend microservices. o Integrated model bias and fairness checks in MLOps pipelines for responsible AI. Developed scalable and type-safe React applications using TypeScript for frontend development. Automated log correlation using DataDog with AWS CloudWatch and OpenShift. Deployed microservices on Linux servers with Docker and Kubernetes. o Built AI pipelines with Scikit-learn and OpenCV for ML and computer vision tasks. Integrated LangChain for LLM applications and conversational AI solutions. Developed and maintained React applications using JavaScript and CSS/HTML for frontend development. Used NumPy broadcasting to optimize memory usage in numerical computations. o Created and tested reusable UI components using JavaScript, Pytest. Created RESTful APIs with Django REST Framework to expose AI models for seamless data exchange. Performed CRUD operations in MongoDB using Mongoose to store and retrieve AI training data efficiently. Utilized SQLAlchemy for ORM-based interactions in Python AI applications, enabling structured AI-driven insights.
o Led end-to-end AI/ML Model Development, including data preprocessing, feature engineering, training, and deployment. Developed and maintained AI-driven Python-based applications for data processing, automation, and intelligent decision-making using TensorFlow, PyTorch, and Hugging Face Transformers. Built scalable web applications integrating FastAPI and AI/ML-powered analytics. o Optimized JavaScript code for AI-powered applications by debugging and refining performance. Built RAG-based AI solutions using Haystack and Milvus. Built data visualization dashboards by combining Pandas with Matplotlib and Seaborn. o Developed hybrid AI systems combining RAG with generative models for dynamic information synthesis. Deployed Deep Learning models on edge devices and mobile platforms for on-device inference. Managed Model Deployment pipelines using Azure Machine Learning Pipelines. Integrated Pandas with Azure Blob Storage for reading/writing large CSV and Parquet files in cloud environments. o Utilized TypeScript with Pytest for unit and integration testing of frontend applications. Implemented RESTful APIs with FastAPI to serve AI inference results, integrating real-time Retrieval-Augmented Generation (RAG) Framework for improved NLP-based responses. Implemented gRPC streaming APIs for handling large-scale data transfers. Automated Linux system configurations using Terraform and Ansible. o Created custom AI/ML Model Development pipelines for NLP, image processing, and structured data analysis. Designed and documented AI-driven API endpoints with FastAPI, ensuring clear documentation for deploying models built with scikit-learn. Optimized PostgreSQL databases for storing vector embeddings and AI-generated metadata. Optimized gRPC request handling using Redis caching.
o Integrated JavaScript with Docker-based microservices for seamless application deployment. Optimized Software Architecture for high-performance AI model serving. Integrated AI models with Apache Spark and Dask for large-scale processing. Integrated TypeScript with Jenkins CI/CD pipelines to automate build and deployment processes. o Developed data preprocessing pipelines using Pandas for financial data analysis and reporting. Optimized Pandas DataFrame operations for handling large-scale datasets in ETL workflows. Developed NumPy-based mathematical models for finance, trading, and risk analysis. Leveraged TypeScript in Node.js microservices for improved API stability and reliability. o Employed Angular’s two-way data binding and component-based architecture to create interactive user interfaces. Developed RESTful APIs with Flask for seamless communication between front-end and back-end systems. Integrated Terraform with Jenkins to enable automated infrastructure provisioning. Integrated PostgreSQL and MySQL databases in cloud environments using Terraform. o Ensured scalable Model Deployment with multi-cloud and hybrid cloud strategies. Implemented rollback strategies for safe Model Deployment in critical systems. Integrated AI pipelines with cloud-native architectures for scalable AI solutions. o Architected and deployed scalable applications using Google Compute Engine (GCE), Google Kubernetes Engine (GKE), and Cloud Functions for optimal performance and flexibility. Managed and optimized data storage solutions with Cloud Storage, Cloud SQL, and BigQuery, ensuring secure and efficient data handling across diverse workloads. o Migrated legacy databases from MySQL to PostgreSQL for scalability and reliability. Applied Classical Machine Learning models to optimize customer segmentation and targeted marketing. Applied NLP models in content moderation and automated compliance checks.