Founding Machine Learning & Backend Engineer at VelocitiPM LLC (2025-06-01 - Present)

Architected a multi-agent system using LangGraph for cyclic state management and CrewAI for role-based task execution. The backend is a FastAPI microservice deployed on AWS AppRunner. To handle high concurrency, implemented an event-driven architecture using AWS Kinesis for stream processing and SNS/SQS for asynchronous task decoupling. Managed agent memory and state persistence using Redis (ElastiCache), specifically optimizing for low-latency retrieval during multi-step reasoning loops. Established MLOps workflows using Amazon SageMaker Pipelines for automated model retraining and MLflow for experiment tracking and model versioning. Monitoring and distributed tracing were handled via AWS X-Ray and CloudWatch to map request flows across the 30-agent swarm.

Skills: Python, Go, C++, LangGraph, CrewAI, FastAPI, Kafka, AWS Lambda, AWS Kinesis, AWS SQS, Redis, MLOps, System Design, Observability. Core Accomplishments: Architected a **30-agent AI orchestration engine** (LangGraph, CrewAI) on AWS AppRunner, automating 80% of PM workflows and increasing engineering throughput **4×**.; Engineered an event-driven, async pipeline using **AWS Kinesis, SNS, and Lambda**, reducing P95 latency by **85%** and ensuring **99.98%** message delivery reliability.; Built a production-grade **MLOps platform** with SageMaker Pipelines and MLflow, automating retraining gates and boosting release cadence **8×**.; Implemented a robust **observability suite** (CloudWatch, X-Ray) that cut critical incident resolution time from 2 hours to **~10 minutes** via distributed tracing.; Designed a **Redis-based memory layer** for agentic state persistence, slashing inter-agent latency by **60%** and supporting **2.5×** higher concurrent user throughput.

Founding AI Engineer at TimelyHero, Dimes Inc. (2024-08-01 - 2025-06-01)

Led the transition from a monolithic architecture to a gRPC-based microservice environment on Azure Kubernetes Service (AKS). Built RAG infrastructure using Apache Airflow for DAG orchestration, Pinecone as the vector database, and MongoDB for metadata storage. To handle real-time user matching, implemented a high-throughput ingestion pipeline using Apache Kafka, focusing on back-pressure strategies and partition tuning to sustain 5,000 messages/sec. Infrastructure was managed entirely through Terraform modules. Observability was achieved by deploying a Prometheus and Grafana stack within the K8s cluster to monitor pod health and WebSocket session persistence.

Skills: Java, Flask, gRPC, Kafka, Airflow, RAG, Pinecone, MongoDB, Terraform, Kubernetes (AKS), Azure, System Design. Core Accomplishments: Spearheaded the migration of a legacy monolith to a **Java/Flask/gRPC microservice architecture** on AKS, scaling to support **100,000+** concurrent WebSocket sessions.; Designed and deployed **Airflow-orchestrated RAG pipelines** (Pinecone, OpenAI, MongoDB), reducing data staleness from 48 hours to **30 minutes** and driving **$250K+** in new enterprise contracts.; Standardized **Infrastructure-as-Code (IaC)** using Terraform and Kubernetes, reducing deployment downtime by **35%** and quadrupling the team's release velocity.; Optimized high-throughput ingestion pipelines with **Kafka back-pressure handling**, stabilizing throughput at **5,000 msgs/sec** under peak loads of 1M+ events/hour.; Implemented a comprehensive **monitoring stack** (Prometheus, Grafana) that improved proactive incident detection by **70%**, ensuring high availability.

Machine Learning Intern at Endimension Inc. (2024-04-01 - 2024-08-01)

Focused on high-performance Computer Vision (CV) model training and deployment for medical diagnostics. Utilized TensorFlow and Keras to train on 8TB of DICOM imagery hosted on S3. Optimized inference performance by converting models to ONNX format and applying 8-bit quantization for deployment on SageMaker multi-model endpoints. Built serverless data preprocessing workflows using AWS Glue and Kinesis for real-time image ingestion. Implemented cost-saving measures using SageMaker Spot Instances for distributed training jobs. Model drift and telemetry were tracked using MLflow and integrated into a CloudWatch dashboard for proactive monitoring.

Skills: TensorFlow, Keras, Computer Vision, Model Optimization, Quantization, ONNX Runtime, AWS SageMaker, CUDA, AWS Kinesis, AWS Glue, MLflow. Core Accomplishments: Trained and optimized large-scale vision models (TensorFlow, Keras) on **8TB of medical imaging data**, improving diagnostic mAP by **13%** and IoU by **17%**.; Deployed **ONNX-quantized inference endpoints** on SageMaker, reducing P95 latency by **40%** with negligible AUC drift (<0.5%) for real-time diagnostics.; Leveraged **SageMaker Spot Instances** and distributed training strategies to reduce GPU training costs by **25%** while accelerating iteration cycles by **1.8×**.; Architected a **serverless inference pipeline** using AWS Kinesis and Lambda, ensuring **24/7 fault tolerance** and scalable processing for high-volume image streams.; Configured a robust **model monitoring stack** (MLflow, Prometheus) to track drift and performance, reducing debugging time for production models by **60%**.

Graduate Research Assistant at JLiang Lab, Arizona State University (2023-09-01 - 2024-05-01)

Conducted large-scale medical imaging research using PyTorch and JAX. Implemented distributed training via PyTorch Fully Sharded Data Parallel (FSDP) on an EKS cluster with A100 GPUs to handle heavy transformer architectures like Swin and DINOv2. Engineered a custom Slurm-based scheduler on AWS EC2 Batch to manage multi-tenant GPU access. Data engineering involved building high-throughput pipelines with AWS Glue to process multi-terabyte datasets stored in S3. Experimentation was strictly versioned using MLflow and SageMaker Experiments to ensure reproducibility in a research environment.

Skills: PyTorch, JAX, Computer Vision, Distributed Training (FSDP), Slurm, AWS EKS, AWS Glue, AWS S3, SageMaker, MLflow. Core Accomplishments: Developed state-of-the-art **multi-modal CV models** (Swin Transformer, DINOv2) on SageMaker GPU clusters, achieving a **24% increase** in rare-disease recall.; Implemented **Fully Sharded Data Parallel (FSDP)** training on Amazon EKS (8× A100s), reducing model training time by **3.2×** and cutting cloud compute costs by **40%**.; Engineered a **multi-node GPU job scheduler** using Slurm and EC2 Batch, automating provisioning and boosting cluster utilization by **35%** across shared workloads.; Built distributed data pipelines with **AWS Glue and S3** to ingest and preprocess **8TB+** of multimodal medical datasets, improving data throughput by **2.6×**.; Designed reproducible experiment tracking workflows with **SageMaker Experiments and MLflow**, reducing hyperparameter tuning time by **45%** and ensuring 100% reproducibility.

Machine Learning Researcher at SRM Advanced Electronics Laboratory (2021-12-01 - 2023-07-31)

Developed a distributed signal processing and regression system for IoT-based medical sensors. The core engine utilized Apache Spark (MLlib) for distributed kernel regression. Engineered the edge-to-cloud bridge using AWS Greengrass and AWS IoT Core, optimizing MQTT protocols for low-latency transmission in constrained network environments. Built robust ETL pipelines using Spark SQL to maintain data integrity from raw sensor signals. The work focused on high-accuracy time-series forecasting and was validated through peer-reviewed publication in Scientific Reports.

Skills: Apache Spark, SQL, Java, AWS Greengrass, AWS IoT Core, AWS MQTT, Regression Modeling, ETL, Research. Core Accomplishments: Developed a distributed **kernel regression pipeline** on Apache Spark (Java + MLlib), achieving a clinical-grade **MARD of 8.86%** for non-invasive glucose monitoring.; Engineered a real-time IoT streaming system using **AWS Greengrass and IoT Core**, enabling ultra-low latency ingestion (**<200ms**) for 2,000+ daily sensor readings.; Designed production-grade **ETL and data quality workflows** with Spark SQL, ensuring **95%+ signal integrity** across distributed edge devices.; Optimized cloud-to-edge messaging protocols via **MQTT**, ensuring reliable data transmission and reducing packet loss by **15%** in unstable network environments.; Co-authored and published this novel research in **Scientific Reports (Nature Portfolio)**, a Q1 journal, validating the system's clinical accuracy and architectural robustness.

Master of Science in Information Technology (AI/ML) from Arizona State University (2023-08-01 - 2025-05-01)

Location: Tempe, AZ, USA. Coursework: Digital Image Processing, Foundations of Statistical Machine Learning, Fundamentals of Machine Learning, Operationalizing Deep Learning, Image Analytics and Informatics, Advanced Operating Systems, Social Media Mining, Knowledge Representation and Reasoning, Cloud Computing, Statistical Machine Learning, Data-Intensive Distributed Systems for Machine Learning. Activities: SoDA: Software Developers Association, ACM Student Chapter, Linux Users Group, The AI Society at ASU, Hindu YUVA.

Bachelor of Technology in Computer Science from SRM University (2019-06-01 - 2023-06-01)

Location: Amaravathi, AP, India. Coursework: Biology, Chemistry, Calculus I, Calculus II, Basic Electronics, Digital Electronics, DSA in C, Physics, Statistics, Discrete Mathematics, OOPs in Java, Linear Algebra, Database Management Systems, Full Stack & Web Technologies, Formal Languages & Automata Theory, Economics, Computer Organization and Architecture, Introduction to Quantum Computations, Differential Equations, Operating Systems, Compiler Design, Data Warehousing and Data Mining, Computer Networks, Data Science, Software Engineering, Fundamentals of Neuro Linguistics Programming, Supply Chain Management, Managing Innovation and Startups, Cloud Computing, Big Data Analytics, Machine Learning. Activities: Founder @ Inventors Village, Founder @ Research Clan, Board Member @SRM Student Council, Board Member @ SRM Entrepreneurship Cell, Member @ GDSC.