KADAVAKOLLU NARASIMHA
Vijayawada, India
KADAVAKOLLU NARASIMHA
Senior AI Platform & Backend Engineer (AWS DevOps)
Category : Artificial intelligence (AI)
I am a Senior AI Platform & Backend Engineer with 3+ years of experience in building production-ready, scalable AI-enabled backend systems. I specialize in bridging the gap between advanced AI modeling and robust cloud infrastructure to deliver high-performance, cost-effective solutions.
My Core Expertise:
AI Infrastructure & RAG: Designing and deploying production-grade RAG pipelines using FastAPI, Qdrant, and Azure OpenAI/AWS Bedrock.
Cloud & DevOps (Expert Level): As an AWS Certified DevOps Professional, I automate infrastructure using Terraform and manage containerized workloads on Kubernetes (EKS/AKS).
Backend Engineering: Developing asynchronous microservices with Python (FastAPI), implementing Redis semantic caching for sub-second latency, and optimizing PostgreSQL performance.
LLMOps & Monitoring: Implementing 30% token cost reduction strategies, responsible AI guardrails, and real-time observability using Prometheus & Grafana.
What I bring to your team:
I don't just write code; I own the end-to-end lifecycle of AI products—from data preparation and experimentation to cloud-native deployment and operational support. I focus on delivering measurable business outcomes by improving code quality, reliability, and cost-per-inference.
I am available for Remote Worldwide roles and can work as an Independent Contractor to provide high-impact technical leadership for your AI platform needs.
My Core Expertise:
AI Infrastructure & RAG: Designing and deploying production-grade RAG pipelines using FastAPI, Qdrant, and Azure OpenAI/AWS Bedrock.
Cloud & DevOps (Expert Level): As an AWS Certified DevOps Professional, I automate infrastructure using Terraform and manage containerized workloads on Kubernetes (EKS/AKS).
Backend Engineering: Developing asynchronous microservices with Python (FastAPI), implementing Redis semantic caching for sub-second latency, and optimizing PostgreSQL performance.
LLMOps & Monitoring: Implementing 30% token cost reduction strategies, responsible AI guardrails, and real-time observability using Prometheus & Grafana.
What I bring to your team:
I don't just write code; I own the end-to-end lifecycle of AI products—from data preparation and experimentation to cloud-native deployment and operational support. I focus on delivering measurable business outcomes by improving code quality, reliability, and cost-per-inference.
I am available for Remote Worldwide roles and can work as an Independent Contractor to provide high-impact technical leadership for your AI platform needs.
Working hours
- Monday:08h00 To 18h00
- Tuesday:08h00 To 18h00
- Wednesday:08h00 To 18h00
- Thursday:08h00 To 18h00
- Friday:08h00 To 18h00
- Saturday:Not available
- Sunday:Not available
I hold a Bachelor’s degree in Computer Science and Engineering, specializing in Artificial Intelligence and Backend Systems. During my academic tenure, I focused on bridging the gap between theoretical computer science and production-grade engineering.
Key Academic & Professional Highlights:
Specialized Coursework: Deep focus on Data Structures, Algorithms (DSA), and Distributed Database Systems.
AI & Machine Learning: Developed hands-on projects involving Natural Language Processing (NLP), RAG (Retrieval-Augmented Generation), and vector search optimization.
Cloud-Native Engineering: Mastered Docker containerization and AWS cloud infrastructure, evidenced by my AWS Certified DevOps Professional credential.
System Design: Practical experience in building asynchronous backend architectures using FastAPI and implementing high-performance caching with Redis.
My education provided a strong foundation in Object-Oriented Programming (Python/Java) and SQL/NoSQL database management, which I now apply to build scalable, international-grade AI platforms.
Key Academic & Professional Highlights:
Specialized Coursework: Deep focus on Data Structures, Algorithms (DSA), and Distributed Database Systems.
AI & Machine Learning: Developed hands-on projects involving Natural Language Processing (NLP), RAG (Retrieval-Augmented Generation), and vector search optimization.
Cloud-Native Engineering: Mastered Docker containerization and AWS cloud infrastructure, evidenced by my AWS Certified DevOps Professional credential.
System Design: Practical experience in building asynchronous backend architectures using FastAPI and implementing high-performance caching with Redis.
My education provided a strong foundation in Object-Oriented Programming (Python/Java) and SQL/NoSQL database management, which I now apply to build scalable, international-grade AI platforms.
- 🇬🇧 English
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