Himansh Mudigonda

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.

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.

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%.

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.

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.