AI Solutions Architect & Full-Stack Engineer
Build Production-Ready AI Systems That Scale
Architect intelligent applications using GPT-4, Claude, Llama 3, and enterprise LLM stacks. Ship production-grade AI agents from concept to deployment in weeks, not months.
$ array deploy --env production
> loading rag-pipeline-v3...
> attaching vector store (pinecone)
> spinning up 4 agent workers
>ready in 2.4s
tokens/s: 2,847 • cost: $0.0031
Meet The Developer
Bridging Cutting-Edge AI Research With Production Reality
I'm Rajesh, a full-stack AI engineer specializing in LLM development, RAG architecture, and autonomous agent systems. I transform complex AI research into robust, scalable products that deliver measurable business outcomes.
Technical Specializations
- 01
LLM Engineering
Fine-tune Llama 3 and Mistral. Architect RAG systems with sub-100ms latency scaling to millions of documents.
- 02
Agent Development
Build autonomous reasoning systems using AutoGen, CrewAI, and semantic orchestration frameworks.
- 03
MLOps Pipelines
Deploy end-to-end workflows from data ingestion through production monitoring and automated retraining.
Technology Stack
Enterprise-Grade AI Infrastructure
Production-hardened technology combining state-of-the-art frameworks, optimized cloud deployment, and intelligent design systems built for scale.
01
LLM Frameworks
LangChain & LlamaIndex • Semantic Kernel • AutoGen & CrewAI • Custom Fine-tuning Pipelines
02
Infrastructure
Vector DBs: Pinecone, Weaviate, Chroma • GPU Cloud: AWS, GCP, Azure • Docker & Kubernetes • CI/CD for LLMOps
03
Development
Python, PyTorch, TensorFlow • PHP, React, Next.js • FastAPI, Node.js • CUDA Optimization
Development Framework
From Concept to Production in 8 Weeks
Execute AI initiatives through a battle-tested methodology delivering production systems rapidly without compromising quality, security, or scalability.
- 01
Strategic Discovery & Architecture Design
Analyze your business domain, map existing workflows, and architect optimal AI solutions whether RAG systems, fine-tuned models, or multi-agent orchestration.
- 02
Rapid Prototyping & Prompt Engineering
Validate concepts within days through functional prototypes using advanced prompt engineering, few-shot learning, and chain-of-thought reasoning patterns.
- 03
Model Fine-tuning & Performance Optimization
Customize open-source models on proprietary datasets. Implement RLHF, LoRA adapters, and custom tokenization for maximum domain-specific performance.
- 04
Production Deployment & LLMOps
Launch with confidence using containerized deployments, Kubernetes orchestration, and comprehensive monitoring for LLM-specific performance metrics.
Service Offerings
AI Development Engineered for ROI
Deliver production-ready AI solutions using the latest LLM technology stack. From intelligent RAG systems to autonomous agents built for performance, scalability, and measurable business impact.
Custom LLM Development & Fine-tuning
Train proprietary models on Llama 3, Mistral, and domain-specific datasets. Build AI that outperforms generic solutions through targeted fine-tuning and RAG integration.
Explore service →Autonomous AI Agent Architecture
Deploy autonomous agents capable of reasoning, planning, and executing complex workflows from research automation to code generation systems.
Explore service →AI-Native Interface Design
Design adaptive interfaces that predict user intent and enable natural conversation. Merge cutting-edge AI capabilities with precision-crafted user experiences.
Explore service →Client Questions
Frequently Asked Questions
Do you partner with early-stage startups?
Absolutely. I specialize in AI MVPs designed to scale. Multiple startup clients have secured Series A funding after shipping their intelligent product.
What does a typical engagement look like?
Engagements range from $15K for focused RAG implementations to $150K+ for comprehensive AI platforms. Fixed-price proposals provided after discovery phase.
Do you provide ongoing LLMOps support?
Yes. Retainer-based LLMOps support includes model monitoring, automated retraining pipelines, and continuous performance optimization.
Let's Build Your Intelligent System
Join forward-thinking teams shipping AI products with custom LLMs and autonomous systems. Trusted by startups and Fortune 500 companies. Prototypes in 2 weeks. ROI-focused delivery.
Technical Insights
Engineering Notes From The Trenches
Deep technical dives into LLM development patterns, RAG architecture decisions, AI agent design, and lessons learned building production AI systems.
RAG vs Fine-Tuning: Which Should You Choose for Your LLM Application?
RAG pulls external data at query time. Fine-tuning bakes knowledge into model weights. This guide breaks down when each approach works, what they cost, and how to pick the right one for your LLM application.
How to Build a Production RAG Pipeline in Python: Step-by-Step with LangChain & Pinecone
A step-by-step tutorial for building a production RAG pipeline in Python using LangChain and Pinecone. Covers document ingestion, chunking, embedding, retrieval, and answer generation with working code.
LLM Fine-Tuning with LoRA and QLoRA: A Practical Guide for Engineers
A hands-on guide to LLM fine-tuning with LoRA and QLoRA. Covers how both techniques work, when to use each, hardware requirements, and a working training example using Hugging Face PEFT.
How to Build an AI Agent from Scratch Using LangChain and OpenAI
Build a working AI agent in Python using LangChain and OpenAI's function calling API. This tutorial covers tool creation, agent loops, memory, error handling, and deployment tips with runnable code.
AutoGen vs CrewAI vs LangGraph: Best Framework for Multi-Agent Systems in 2025
A direct comparison of AutoGen, CrewAI, and LangGraph for building multi-agent AI systems. Covers architecture, ease of use, production readiness, and which framework fits which use case.
Pinecone vs Weaviate vs Chroma: Choosing the Right Vector Database for Your AI App
A practical comparison of Pinecone, Weaviate, and Chroma for AI applications. Covers pricing, performance, ease of use, managed vs self-hosted options, and which vector database fits your project size.
What Is LLMOps? The Complete Guide to Operating LLMs in Production
LLMOps is the set of practices for deploying, monitoring, and maintaining large language models in production. This guide covers the LLMOps stack, tooling, and the operational problems most teams hit after launch.
How to Reduce LLM API Costs by 80% Without Sacrificing Quality
Practical techniques to cut LLM API costs by up to 80%. Covers prompt compression, caching, model routing, batching, and cheaper model substitution with real cost comparisons.
Deploying Llama 3 on AWS: A Production-Ready Setup Guide
How to deploy Llama 3 on AWS for production inference. Covers instance selection, vLLM setup, autoscaling, load balancing, and cost comparison with API-based models.
Prompt Engineering Best Practices: Techniques That Actually Work in Production
Prompt engineering techniques that hold up in production, not just demos. Covers system prompts, few-shot design, output formatting, chain-of-thought, and common mistakes that waste tokens and degrade quality.
How to Evaluate Your LLM: Benchmarks, Metrics, and Testing Frameworks Explained
How to evaluate LLM outputs for accuracy, hallucination, and quality. Covers popular benchmarks, practical metrics, LLM-as-judge approaches, and testing frameworks you can run today.
Building a Multi-Agent AI System: Architecture Patterns and Pitfalls to Avoid
Architecture patterns for building multi-agent AI systems that work in production. Covers supervisor, swarm, and pipeline patterns with tradeoffs, failure modes, and practical design decisions.
Start The Conversation
Let's Architect Your AI Solution
Currently accepting select clients for Q2 2025. Share your project requirements and receive a technical proposal within 48 hours.
info@array.im
Guaranteed response within 24 hours