Abhishek G.

I help enterprises move AI from prototype to production — solving the problems that appear when AI systems become **expensive, slow, unreliable, and difficult to scale.** With 12+ years across Software Engineering, Technical Leadership, CTO, and AI Architecture, I design production-grade AI systems, agentic workflows, enterprise RAG platforms, and AI-native SaaS products. My focus is not simply building chatbots. I solve the engineering problems behind reliable AI: • Runaway token costs • Slow agent execution • Excessive LLM calls and agent loops • Poor RAG retrieval and hallucinations • Chunking and embedding problems • Context-window and memory problems • Unreliable multi-agent systems • AI infrastructure and scaling • Observability, evaluation and cost control ━━━━━━━━━━━━━━━━━━━━━━ 🚀 AI AGENT ARCHITECTURE I design production-ready: • Multi-agent systems • Planner → Supervisor → Worker architectures • Tool calling and API orchestration • MCP-based systems • Agent memory and state management • Human-in-the-loop workflows • Long-running/background agents • Event-driven agents • Agent evaluation, guardrails and failure recovery Technologies: Claude, OpenAI, AWS Bedrock, AgentCore, LangGraph, LangChain, MCP, n8n, Python, TypeScript, FastAPI, Node.js. ━━━━━━━━━━━━━━━━━━━━━━ 💰 AI TOKEN COST OPTIMIZATION AI systems can become extremely expensive at production scale. I reduce unnecessary: • Prompt/context tokens • Conversation history • RAG context • Tool outputs • Agent-to-agent communication • LLM calls and reasoning loops Using context compression, prompt caching, model routing, retrieval optimization, structured outputs, summarization and intelligent model selection. The goal isn't simply **"use a cheaper model."** It is to reduce inference cost while maintaining quality and reliability. ━━━━━━━━━━━━━━━━━━━━━━ ⚡ AGENT LATENCY OPTIMIZATION Slow agents are often an architecture problem. I analyze: User → API → Agent → LLM → Retrieval → Tools → APIs → Database → Response And optimize: • Sequential → parallel execution • Unnecessary LLM calls • Tool-call chains • RAG/vector-search latency • Database/API bottlenecks • Network round trips • Model selection • Streaming, caching and async execution ━━━━━━━━━━━━━━━━━━━━━━ 🧠 CONTEXT ENGINEERING Many AI systems fail because the model receives the **wrong context**. I design architectures for: • Short/long-term memory • Conversation history • Retrieved knowledge • Task and agent state • Tool results • Dynamic context • Context compression and prioritization The key questions are: **What should the model know? When should it know it? How much does it actually need?** ━━━━━━━━━━━━━━━━━━━━━━ 🔎 ENTERPRISE RAG & RETRIEVAL I build RAG systems optimized for **accuracy, latency, cost, security and traceability.** I solve: • Poor retrieval and irrelevant chunks • Hallucinations • Large context windows • Slow vector search • Metadata filtering • Multi-tenant / permission-aware retrieval • Citations and provenance • Embedding and reranking problems Typical pipeline: Document → Parsing → Chunking → Embedding → Hybrid Search → Metadata Filter → Reranking → Context → LLM Technologies: Pinecone, OpenSearch, Elasticsearch, pgvector, Weaviate, FAISS. ━━━━━━━━━━━━━━━━━━━━━━ ✂️ CHUNKING & EMBEDDING OPTIMIZATION Chunking is often a hidden cause of poor RAG performance. I work with: • Fixed, sentence, paragraph and semantic chunking • Parent-child retrieval • Document/code-aware chunking • Metadata enrichment • Overlap optimization • Embedding selection • Top-K tuning • Reranking The objective isn't smaller chunks — it's creating retrieval units that preserve the meaning needed to answer the question. ━━━━━━━━━━━━━━━━━━━━━━ 🏗️ AI AGENT INFRASTRUCTURE I design infrastructure for reliable, scalable agent execution: • Agent runtime and background jobs • Queues and state/checkpointing • Retries and idempotency • Concurrency and rate limiting • Observability and tracing • Cost monitoring • Multi-tenancy and security • Failure recovery AWS: Bedrock, AgentCore, Lambda, ECS, EKS, API Gateway, ALB, SQS, EventBridge, Step Functions, DynamoDB, Aurora, S3, OpenSearch, CloudWatch. ━━━━━━━━━━━━━━━━━━━━━━ 🤖 CLAUDE CODE / AI-NATIVE ENGINEERING I use **Claude Code as an AI engineering layer**, not just a code generator. Capabilities include: • Large codebase understanding • Architecture analysis • Refactoring and modernization • Feature implementation • Debugging and test generation • Code review • Repository intelligence • MCP integrations • Engineering automation I help teams evolve from traditional development toward **AI-native software engineering.** ━━━━━━━━━━━━━━━━━━━━━━ 🔄 AI APPLICATION MODERNIZATION I help existing SaaS and enterprise applications become AI-native: **Legacy Application → APIs → AI Layer → Agents → Knowledge → Automation** Including AI copilots,