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English
$
USD
I will deploy scalable rag ai infrastructure for lab or production
Stephen Oduor
About this gig
What You Get:
Fully configured RAG AI Infrastructure (Retriever + LLM + Vector Store + API layer)
Deployment on AWS, Google Cloud (GCP), or Microsoft Azure
Infrastructure managed via Kubernetes (EKS, GKE, or AKS)
Integration with tools like LangChain, LLamaIndex, Pinecone, Weaviate, FAISS, or your preferred vector DB
CI/CD pipelines for scalable, repeatable deployments
Optional: API Gateway, Authentication, Monitoring, and Logging setup
️
Use Cases:
- AI-powered internal knowledge bases
- Chatbots that understand your documentation
- Semantic search for enterprise data
- R&D Lab environments for experimentation
- Production-ready AI platforms with full MLOps
Tech Stack (Can be customized):
- LLM: OpenAI, Anthropic, Hugging Face models, etc.
- Vector DB: FAISS, Pinecone, Chroma, Weaviate, etc.
- LangChain / LLamaIndex / RAG Stack
- Kubernetes: EKS / GKE / AKS
- Terraform / Helm / ArgoCD / GitOps (on request)
Why Choose Me?
Im a DevOps + AI Engineer with hands-on experience in spinning up cloud-native, scalable, and cost-efficient RAG architectures for startups and enterprises. I work closely with you to deliver tailored, secure, and future-ready solutions.
Get to know Stephen Oduor
Stephen Oduor
Software Engineer : DevOps and Cloud Consultant
- FromKenya
- Member sinceJan 2017
- Avg. response time1 hour
- Last delivery1 year
Languages
English, Swahili
With 8+ years in Software & DevOps and Engineering, I architect 99.9% reliable ecosystems for fintech and global health. Expert in Kubernetes, Docker, and multi-cloud (AWS/GCP/Azure) with Terraform automation.
Key Services:
Full-Stack Dev: Custom React/Vue frontends and robust Python (Django), Java (Spring Boot), or Laravel backends.
Enterprise Platforms: Expert deployment of KoboToolbox, DHIS2, and Apache Fineract.
AI DevOps: Optimizing AI/ML lifecycles and infrastructure reliability.
Visual Design: High-fidelity Figma prototyping and intuitive dashboards.
My Portfolio
FAQ
Q1: What is RAG AI, and why should I use it?
A: RAG (Retrieval-Augmented Generation) is a powerful AI architecture that combines large language models (LLMs) with external knowledge sources (like vector databases) for more accurate, up-to-date, and context-aware responses. It’s perfect for chatbots, document search, and AI assistants.
Q2: Can you deploy on any cloud provider?
Yes! I support AWS, Google Cloud Platform (GCP), and Microsoft Azure. I also use Kubernetes (EKS, GKE, or AKS) for scalable, cloud-native deployment.
Q3: What components are included in the deployment?
A typical deployment includes: - LLM integration (OpenAI, Hugging Face, etc.) - Vector database (e.g., FAISS, Pinecone, Chroma) - API and retrieval logic (LangChain or LlamaIndex) - CI/CD (optional) - Kubernetes orchestration - Monitoring and logging (on request)
Q4: Can you set this up for sandbox/testing environments?
Absolutely! I can set up lightweight environments for experimentation and R&D, as well as hardened, production-ready systems.
Q5: Will I be able to maintain the setup afterward?
Yes. I provide documentation, walkthroughs, and optionally a video demo to help your team operate the system independently.
Q6: Can I request a custom configuration?
Definitely. Every business has unique needs—just message me before placing an order, and I’ll tailor a setup specifically for you.
