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Prasad Adireddi

Angestellt, Cloud Engineering, Hewlett Packard, Hyderabad, India
Berlin, Deutschland

Fähigkeiten und Kenntnisse

AI Platform Engineering
GenAI LLM & RAG Workloads
MLOps Pipelines & AI/ML Lifecycle Management
Model Serving Inference & GPU Acceleration
CI/CD Automation & Infrastructure as Code (Terraform)
Cloud-Native & Kubernetes Architecture
Roadmaps & Executio
Customer-Centric Platform & Solution Delivery
BigData
DevOps / Cloud
Communication skills
Project Management
Team work
MS Office
Machine Learning
MLflow
Microsoft Azure
Software Development
Google Cloud
AWS
Deployment
GitHub
Administration
Bash (Unix shell)
Python
GitLab
Data Science
GMP
Security Policy
Consulting
Management
Corporate Governance
Documentary
Agile Development
Databricks
Certification
Docker
Bitbucket
ETL
Data Preparation
Compliance
Red Hat Enterprise
Jenkins
cloud
cloud engineer
AI/ML & GenAI Skills
Cloud-Native & Distributed Systems
Platform
Performance Management
English Language
Windows Power Shell
Windows
Linux
Spearheaded
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Drove
Delivered
Executed
Enterprise AI Platform Engineering
MLOps & AI Lifecycle Management
Model Serving & Inference Platforms
AI Agent Frameworks & Knowledge Systems
Data Engineering & Distributed Data Platforms
Feature Engineering & Model Operationalization
GPU-Accelerated AI Infrastructure
Distributed Systems Engineering
Infrastructure as Code
Platform Reliability & Scalability
Open-Source Platform Ecosystems
Observability & Performance Engineering
Cloud & Distributed Systems
AI / Platform Engineering
Operationalized
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Engineering Excellence
Software Architecture & System Design
CI/CD & DevSecOps
Platform Automation & Self-Service Enablement
Engineering Standards & Governance
Cross-Functional Program Execution
Kubernetes & Cloud-Native Platforms
AI/ML & Data Platforms
AI Platform Architecture
Enterprise GenAI Platforms
LLMOps
RAG Architectures
AI Infrastructure
GPU Platform Engineering
AI Governance
Kubeflow
KServe
Triton
Seldon
Model Registry
Feature Stores
Continuous Training
AI Monitoring
Distributed Systems & Cloud Architecture
Kubernetes at Scale
Multi-cluster Architecture
Service Mesh
Cloud Native Design
OpenShift
Hybrid Cloud
Multi-Tenant Platforms
High Availability Systems
GenAI & LLM Expertise
LLM Architecture
Agentic AI
Multi-Agent Systems
RAG Pipelines
Embedding Models
Vector Databases
Prompt Engineering
Guardrails
AI Safety
NVIDIA NIM

Werdegang

Berufserfahrung von Prasad Adireddi

  • Bis heute 4 Jahre und 6 Monate, seit Apr. 2022

    Cloud Engineering

    Hewlett Packard, Hyderabad, India

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