Prateek Parihar

About Prateek Parihar

Senior Generative AI and Cloud Architect with 12+ years building production-grade systems. Journey: Hands-on development → Cloud scaling & system architecture → GenAI, RAG & agentic systems. EARLY CAREER: Developed cloud-native code and APIs at scale. Built serverless architectures on AWS for millions of daily events. Scaled distributed systems for high-performance, fault-tolerant applications. CLOUD ARCHITECTURE ERA: Designed cloud solutions for VW, FCA, and telematics platforms. Architected event-driven systems (Kinesis, Lambda, DynamoDB) processing 2M+ daily IoT events. Implemented infrastructure-as-code, CI/CD automation, and observability at enterprise scale. GENAI TRANSFORMATION (NOW): Currently architecting GenAI platforms at Magna. Building RAG pipelines, LLM orchestration, and AI agents. Designed PlannerAI—a Kubernetes-native platform reducing supplier onboarding from 8 weeks to 3 weeks. Implemented zero-trust security for AI inference. Enabled 20+ teams to self-serve GenAI capabilities. CORE EXPERTISE: • Development: Python, Java, Node.js. REST APIs, microservices, gRPC, system design • Cloud Platforms: AWS expert (Lambda, Bedrock, EKS, Kinesis), Azure, Kubernetes, Serverless • GenAI: LLMs, RAG, AI Agents, prompt engineering, fine-tuning, multi-agent orchestration • MLOps: Model registries, deployment pipelines, monitoring, cost optimization • Infrastructure: Terraform, CI/CD, observability, SLOs, zero-trust security SEEKING: Senior Architect or Solutions Architect or Software Engineer roles in GenAI, Cloud Architecture, or AI/ML. Open to startups, consulting, and transformative projects. Bachelor\'s in Computer Science. Continuous learner. Combining technical depth with strategic thinking to solve complex challenges at scale.

Experience

1. Solutions Architect

   Magna International

   May 2024 — Present

   Architected and deployed PlannerAI, a Kubernetes-native GenAI platform for material resoure planning data ingestion and standardization. Leveraged RAG pipelines with vector embeddings, LLM orchestration (Claude, AWS Nova), and custom AI agents to reduce plant PFEP (Plan for each part) onboarding cycles from 4 weeks to 1 weeks- Designed end-to-end MLOps infrastructure with model versioning, prompt management, evaluation frameworks, and automated deployment pipelines. Implemented monitoring for GenAI use cases with 99.5% reliability- Led zero-trust security architecture using Ory and Open Policy Agent (OPA) for fine-grained ABAC across AI inference workloads. Secured both SaaS and on-premises deployments with audit trails for enterprise compliance- Enabled 20+ internal teams to rapidly deploy GenAI use cases through golden path templates and platform SDKs. Reduced time-to-market for new AI initiatives by 60% and standardized LLM integration patterns- Mentored engineers on LLM deployment best practices, prompt engineering techniques, and RAG implementation patterns.

Education

• College of Technology and Engineering,Udaipur,India

  Bachelor of Engineering (B.E.), Information Technology

  2009 — 2013

Skills

• Javascript
• Node.js
• C
• Mysql
• Cloud Computing
• Xml
• Ecmascript
• Html
• Java
• Microservices
• Jsp
• Phonegap
• Web Services
• Troubleshooting
• Amazon Web Services (Aws)
• Angularjs
• C++
• Ionic Framework
• Apache Cordova
• Sql
• Restful Webservices
• Linux