I'm a Founding Engineer & AI Architect with 2.5 years of hands-on experience building AI-native, distributed backend, and high-scale full-stack systems. I lead AI Architecture & Platform Engineering @ProPeers, owning core products like RoadmapAI, CodeLLM, AskAI, Global AI Search and the Contextual AI Code Editor powering 80%+ of total platform traffic and enabling real-time learning for 600K+ users.

I architect Agentic AI Ecosystems combining Azure OpenAI Service, Azure Databricks, GPT reasoning models, Llama 3.x models, and Databricks-hosted OSS models. My work spans multi-model routing, context-window compression, semantic chunking, token optimization, and dynamic prompt engineering to build low-latency, production-grade inference systems.

LLM MODELS and AI platforms including OpenAI, Google AI, Anthropic (Claude), MistralAI, Grok, Meta, Moonshot and Databricks,implementing intelligent model routing,fallback strategies,cost-aware inference, andlatency-optimized multi-provider execution.

I’ve implemented token-level streaming inference across user-facing AI systems to deliver real-time, low-latency responses, significantly improving perceived performance and interaction smoothness. Alongside this, I designed model-response caching layers, embedding reuse, and prompt-result memoization strategies to minimize redundant LLM calls, reduce token consumption, and optimize cost-efficiency under high-concurrency production workloads.

I design Adaptive RAG Systems using LangChain, ChromaDB, and custom vector search pipelines delivering <1s semantic retrieval with self-learning RAG enrichment, confidence scoring, and progress-aware context routing.

I’ve engineered RoadmapAI end-to-end with a self-learning RAG pipeline (text-embedding-ada-002, ChromaDB, semantic filters, adaptive difficulty), achieving ~99% roadmap accuracy and significantly improving user ratings from the early 12% baseline.

I built CodeLLM, a production AI judge with multi-language detection, dual-layer JSON parsing, COMPILATION/RUNTIME/VALIDATION error classification, semantic retrieval, and deterministic verdict synthesis.

I developed AskAI, an agentic assistant using MCP-layered prompts, resource-type detection, O1/O3 routing, token metering, and auto-structured responses improving resolution speed 2× and engagement 3×.

I also engineered the AI Code Editor with ~40ms inference, inline reasoning, multi-language execution, and deep integration with RoadmapAI and CodeLLM, boosting editor retention by 40%.

Beyond AI flows, I build real-time backend architectures, Redis-backed caching layers, aggregation engines for 600K+ users, search-validation systems, role-based access frameworks, and CI/CD pipelines that reduced release time by 34%. I’ve optimized system performance using SSR, dynamic imports, and hybrid rendering patterns, cutting user response times from 1.1s → 200ms.

I’ve implemented token-based tiered access systems, self-optimizing RAG pipelines, and distributed multi-model inference workflows that balance accuracy, latency, and cost under real-world production workloads.

I’ve engineered the core of our AI ecosystem Multi LLM Orchestration, ARX (AI Architect), Versus, Voice with 24+ Brains, AskDSA, RoadmapAI, CodeLLM, AskAI, AI Code Editor, and the Global AI Search, designing end-to-end Agentic AI pipelines with RAG-driven personalization, MCP-layered orchestration, and multi-model LLM architectures that deliver real-time learning guidance, deterministic code evaluation, and deeply context-aware programming assistance at scale.

I’ve worked hands-on with leading LLM MODELS and AI platforms including OpenAI, Google AI, Anthropic (Claude), MistralAI, Meta, Grok, Moonshot and Databricks, implementing intelligent model routing, fallback strategies, cost-aware inference, and latency-optimized multi-provider execution.

The system leverages Vector Databases (ChromaDB) for semantic context retrieval and long-term memory, enabling high-precision RAG workflows. I’ve implemented token-level streaming responses to deliver real-time AI output, along with response caching, embedding reuse, and prompt-result memoization to significantly reduce latency, repeated inference, and overall token costs.

My work spans LLM System Design, tokenization & reasoning flows, streaming & tool-calling agents, vectorized context pipelines, and high-availability AI microservices, forming the intelligence backbone of the platform.