B. Anil Kumar
Senior Full Stack & Generative AI Engineer with 8+ years of expertise building enterprise-scale platforms, AI-powered systems, and multi-cloud solutions using Python, FastAPI, React.js, Node.js, Google Cloud (Vertex AI, Gemini, Cloud Run), AWS, and Docker. Proven specialist in orchestrating multi-agent RAG pipelines, LangGraph workflows, AI agent evaluation (Ragas, TruLens, LangSmith), vector retrieval benchmarks, and automated CI/CD DevOps architectures.
Stateful multi-agent supervisor loops with deterministic state transitions.
Anil is a Senior Full Stack & Generative AI Engineer with 8+ years experience specializing in LangGraph multi-agent systems, RAG & Ragas evaluation, Google Cloud (Vertex AI), and AWS cloud microservices.
Work Experience & Leadership
8+ years architecting mission-critical platforms, pioneering Generative AI applications, and engineering scalable cloud microservices.
Senior Software Engineer
Key Architectural Contributions
Design and engineer scalable enterprise applications utilizing React, Python, FastAPI, Google Cloud (Vertex AI), and AWS cloud infrastructure.
Build production-grade Generative AI solutions, AI Agents, and multi-agent workflows leveraging LangChain, LangGraph, and Gemini 1.5 Pro.
Implement comprehensive AI Agent Evaluation suites using Ragas, TruLens, and LangSmith for automated RAG faithfulness and tool trajectory testing.
Implement high-performance semantic search and knowledge retrieval engines using vector databases (FAISS, ChromaDB, Vertex AI Vector Search).
Automate enterprise CI/CD pipelines with Docker, GitHub Actions, Google Cloud Run, and resilient AWS microservices deployment.
Key Enterprise Architectures
Proprietary agentic platforms engineered with stateful LangGraph workflows, high-density vector retrieval, and automated cloud deployments.
Enterprise AI Knowledge Assistant
Multi-agent RAG platform supporting automated multi-format document ingestion (PDF, DOCX, Excel) and low-latency semantic contextual retrieval across complex enterprise repositories.
Sub-second query retrieval with 98% precision across 50,000+ enterprise documents.
AI Release Impact Analysis & Agent Testing Platform
Autonomous multi-agent platform that analyzes RFCs, release notes, code diffs, and change requests to perform automated impact analysis, risk assessment, and dependency graph mapping with LangSmith regression testing.
Enterprise Multi-Cloud Release Automation Platform
Microservices-based release orchestration engine featuring multi-tier approval workflows, automated deployment monitoring, health check verifications, and multi-cloud container rollouts.
AI Agent & RAG Evaluation Studio
Rigorous testing frameworks ensuring deterministic agent execution, zero hallucination, and sub-200ms latency across Google Cloud (Vertex AI) & AWS.
RAG Triad & Faithfulness Eval Suite
[INFO] Initializing Ragas Evaluation Suite with test dataset (n=2,400)
[EVAL] Generating embeddings with Vertex AI textembedding-gecko@003
[EVAL] Calculating Context Precision across top-5 hybrid retrieval chunks: 0.972
[EVAL] LLM-as-a-Judge Faithfulness Verification (Gemini 1.5 Pro): Grounded 98.4%
[ASSERT] All 4 RAG Triad thresholds PASSED. Zero hallucination detected.
Core Engineering Competencies
Generative AI & Agent Architectures
AI Agent Testing, Eval & Guardrails
Google Cloud (GCP) & Cloud Platforms
Backend & Microservices Engineering
Frontend, Web & Visualization
Databases, Vector Stores & Caching
Education & Engineering Qualifications
Bachelor of Engineering (Information Technology)
Vasavi College of Engineering
Specialized in Software Engineering, Distributed Systems, Data Structures, and Algorithm Design.
Diploma in Computer Science Engineering
Mahaveer Institute of Science and Technology
Foundational studies in Computer Systems, Microprocessors, Operating Systems, and Object-Oriented Programming.
Get In Touch & Connect
Open for Senior Full Stack & Generative AI engineering roles, technical architecture consulting, and advisory collaborations.
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