Akhil Reddy Danda

Director of Engineering

Seattle, Washington, United States4 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Led AI platforms processing over 100GB daily.
Achieved 23% model accuracy improvement through MLOps.
Scaled microservices to over 100K requests per second.

Stackforce AI infers this person is a Healthcare AI Engineer with expertise in distributed systems and machine learning.

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Skills

Core Skills

Machine LearningCloud ComputingDistributed SystemsBioinformatics

Other Skills

Azure pipelinesLLM applicationsscalable system designpredictive analyticsautomated reporting systemsCNNsdeep learning techniquesautomated bioinformatics pipelinesKerasConvolutional Neural Networks (CNN)YOLOImage ProcessingObject DetectionSciPyAutonomous Vehicles

About

Ex-Amazon → Now building healthcare AI that impacts real patients. I'm a Software Engineer who thrives where distributed systems meet machine learning. 🔹 Scaled microservices to 100K+ req/s at Amazon 🔹 Now leading AI platforms processing 100GB+ daily at HHA Medicine 🔹 23% model accuracy improvement through MLOps I don't just call APIs, I understand what's under the hood. Transformers & attention mechanisms. GRPO & LoRA fine-tuning. Diffusion models & denoising score matching. Linear regression to neural network convergence. Math + Systems + ML = Production AI that actually works. Building in public. Always shipping.

Experience

4 yrs 2 mos

Total Experience

2 yrs 1 mo

Average Tenure

2 yrs 10 mos

Current Experience

Hha hospital medicine

3 roles

Director of Information Technology

Promoted

Sep 2025 – Present · 8 mos · Corpus Christi, Texas, United States · On-site

Senior Software Engineer

Promoted

Feb 2024 – Sep 2025 · 1 yr 7 mos · Corpus Christi, Texas, United States · On-site

Currently leading technical direction for enterprise-grade AI and cloud platforms supporting HHA Hospital Medicine’s rapid growth, architecting systems that scale with company expansion and streamline infrastructure for new hospitals

SDE

Jul 2023 – Mar 2024 · 8 mos · Corpus Christi, Texas, United States · On-site

Built cloud-native, AI-driven systems powering company’s rapid expansion across hospitals, focusing on Azure pipelines, LLM applications, and scalable system design for actionable finance, revenue, and operations insights. Implemented ML models for denial prediction, patient forecasting, and outcome analysis, improving operational decision accuracy by 20%+ through predictive analytics and automated reporting systems

Azure pipelinesLLM applicationsscalable system designpredictive analyticsautomated reporting systemsMachine Learning+1

Amazon

SDE

Feb 2022 – Jun 2023 · 1 yr 4 mos · Greater Seattle Area · On-site

Machine LearningDistributed Systems

Texas a&m university

Machine Learning Researcher

Sep 2020 – Dec 2021 · 1 yr 3 mos · United States

Developed ML models for protein-DNA interaction analysis, achieving 89% accuracy in structure-function predictions using CNNs and deep learning techniques for gene editing research
Built automated bioinformatics pipelines using Python, TensorFlow, and BioPython to process 10TB+ genomic sequencing data for large-scale computational biology analysis
Investigated protein modification techniques for enhanced stability and therapeutic applications, contributing to university’s applied AI research initiatives in biotechnology
Applied machine learning approaches to optimize protein synthesis pathways and analyze binding affinities, strengthening foundation for scalable AI-driven solutions in industry

CNNsdeep learning techniquesautomated bioinformatics pipelinesMachine LearningBioinformatics