Girish Malapati

AI Researcher

Bengaluru, Karnataka, India2 yrs 10 mos experience

Key Highlights

Expert in deploying production-grade ML systems in healthcare.
Proficient in building CI/CD pipelines for continuous model improvement.
Skilled in developing low-latency inference services using modern tech.

Stackforce AI infers this person is a Healthcare-focused Machine Learning Engineer with strong MLOps capabilities.

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Skills

Core Skills

Cloud ComputingMachine LearningContinuous Integration And Continuous Delivery (ci/cd)Programming

Other Skills

DockerPython (Programming Language)PyTorchDeep LearningAWSMicroservicesCI/CDMLOpsFastAPIArtificial Neural NetworksConvolutional Neural Networks (CNN)Supervised LearningUnsupervised LearningModel BuildingC++

About

Machine Learning and MLOps Engineer with 2.5+ years of experience building and deploying production-grade computer vision and deep learning systems in regulated healthcare environments. I specialize in translating research-driven models into scalable, low-latency inference services using PyTorch, FastAPI, Docker, and AWS. My work spans the full ML lifecycle from custom model development and training to CI/CD automation, cloud-native deployment, and feedback-driven continuous improvement. I have hands-on experience developing lightweight CNN models for medical imaging, deploying segmentation services on AWS ECS Fargate, and designing Human-in-the-Loop (HITL) pipelines to capture model failure modes and enable retraining and drift analysis. I enjoy working at the intersection of machine learning, systems engineering, and production infrastructure to build reliable AI solutions.

Experience

2 yrs 10 mos

Total Experience

2 yrs 8 mos

Average Tenure

2 mos

Current Experience

Tiger analytics

AIML Engineer

Mar 2026 – Present · 2 mos · Bengaluru · Hybrid

Toshiba software (india) pvt. ltd.

3 roles

Software Engineer

Jul 2025 – Mar 2026 · 8 mos

DockerCloud Computing

Associate Software Engineer

Jul 2023 – Jul 2025 · 2 yrs

Developed a custom lightweight CNN in PyTorch to classify stent marker vs non-marker candidates from 32×32 grayscale fluoroscopy image patches, achieving 85–95% validation accuracy on small medical datasets.
Optimized model architecture and inference pipeline for low-latency, real-time deployment in clinical imaging workflows.
Deployed a highly available, multi-container microservices architecture on AWS ECS Fargate to serve a PyTorch-based MedSAM image segmentation service.
Built zero-downtime CI/CD pipelines using GitHub Actions and Docker path filtering, and implemented Human-in-the-Loop feedback systems for continuous model improvement and drift analysis.
Built a low-latency C++ image preprocessing and data transfer interface for deep learning–based medical image reconstruction systems.
Implemented optimized data pipelines and socket-based communication to support real-time, performance-critical image processing.

Machine LearningPython (Programming Language)

Intern

Jan 2023 – Jul 2023 · 6 mos

Developed an interactive 3D medical image viewer in Python using SimpleITK, enabling navigation across multiple planes and adjustable contrast/brightness.
Built a comparison feature to analyze and consolidate differences between two imaging outputs, aiding radiologist workflows.

Python (Programming Language)Programming