Prasanth

Machine Learning Engineer - AI Product Studio

(2025-06)

Built multi-agent AI applications using Python, LangChain, LangGraph, GPT-4, and Claude to automate document analysis and workflow orchestration, improving response consistency across enterprise business processes.
Implemented Retrieval-Augmented Generation (RAG) pipelines using Pinecone, FAISS, hybrid search, and embedding models, improving retrieval precision by approximately 30% and reducing hallucinations in enterprise knowledge systems.
Delivered scalable AI microservices using FastAPI and Flask with asynchronous REST APIs, reducing inference latency by approximately 35% while enabling reliable model inference and enterprise application integration.
Optimized distributed PySpark, Airflow, and dbt data pipelines that processed large-scale datasets for AI applications, improving pipeline reliability and reducing data preparation time.
Automated MLOps workflows using MLflow, SageMaker Pipelines, Docker, Kubernetes, Jenkins, and GitHub Actions, streamlining model versioning, validation, and continuous deployment across environments.
Deployed cloud-native AI services on AWS using SageMaker, Lambda, EC2, API Gateway, S3, and DynamoDB, enabling scalable inference, reliable service integration, and production-ready deployments.

Applied AI Engineer - Morgan Stanley

(2024-07 - 2025-05)

Engineered end-to-end machine learning pipelines spanning data ingestion, feature engineering, model training, deployment, and monitoring, reducing model delivery time by approximately 40% across financial risk analytics workflows.
Delivered LLM-powered multi-agent solutions using LangChain, LangGraph, GPT-4, Claude, and AWS Bedrock to automate financial document analysis and strengthen compliance review workflows.
Optimized distributed PySpark and SQL data pipelines processing large-scale financial datasets, improving data reliability and reducing ETL processing latency for downstream analytics.
Automated ML lifecycle management using MLflow, SageMaker Pipelines, Docker, Kubernetes, and Jenkins, reducing model deployment effort by approximately 50% while standardizing CI/CD workflows.
Integrated AWS services including S3, Lambda, and Bedrock with AI applications to support secure, scalable inference pipelines and enterprise-grade model deployment.

Associate Engineer - Virtusa

(2022-06 - 2023-06)

Modernized backend services by implementing scalable RESTful microservices using FastAPI and Flask, improving application performance and supporting reliable enterprise integrations.
Engineered distributed ETL pipelines using PySpark and SQL to process large-scale enterprise datasets, improving data pipeline efficiency and supporting downstream reporting and business applications.
Containerized backend services using Docker and Kubernetes, streamlining deployment workflows and improving application reliability across development and testing environments.
Enhanced REST API performance through query optimization, efficient data access patterns, and backend improvements, reducing response times for enterprise applications.
Strengthened application quality by implementing automated testing with PyTest and monitoring services with AWS CloudWatch, improving system reliability and accelerating issue detection.

Software Engineer - Cognizant

(2021-05 - 2022-05)

Developed backend services and RESTful APIs using Python and Flask, enabling secure communication between enterprise applications and improving application maintainability.
Optimized distributed ETL pipelines using PySpark and SQL to process large-scale enterprise datasets, improving data consistency and supporting downstream reporting workflows.
Deployed backend applications on AWS using EC2, Lambda, API Gateway, and DynamoDB, improving scalability and supporting reliable cloud-based application deployments.
Streamlined application delivery by implementing CI/CD pipelines using Jenkins and GitHub Actions, improving release consistency and reducing manual deployment effort.
Containerized backend applications using Docker and collaborated on Kubernetes-based deployments, improving deployment consistency across development and testing environments.

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