Abhilash V.

I architect, build and deploy production-grade agentic AI systems, enterprise RAG platforms and document-intelligence applications using Python, FastAPI, LangGraph and privately hosted or managed LLMs. ✅ 9+ years in AI and machine learning engineering ✅ Upwork Top Rated with 100% Job Success ✅ Completed and delivered projects for global clients ✅ Ranked in the Top 10% of Kaggle competitors ✅ Worked with teams across the North America, Europe and East Asia ✅ Reduced manual review time by 50% and improved retrieval relevance by 35% in production. My work includes enterprise architecture-review agents, regulated document-verification platforms, multimodal intake systems and source-grounded assistants that analyse complex files and return structured findings with citations. CORE CAPABILITIES • Multi-agent architecture and stateful orchestration • Enterprise RAG, document intelligence and multimodal analysis • Evaluation datasets, guardrails and regression testing • FastAPI, PostgreSQL, pgvector and asynchronous processing • Private LLM deployment with vLLM and Kubernetes • Model routing, inference optimization and observability Most AI systems do not fail because the model is incapable. They fail because nobody defines what a correct result looks like. I establish the evaluation dataset and acceptance criteria early, test releases against them, guard inputs, outputs and tool calls, and require human approval before consequential actions. 🧠 PRODUCTION MULTI-AGENT SYSTEMS With LangGraph, I implement state machines, parallel specialist agents, conditional routing, retries, escalation, human checkpoints and PostgreSQL-backed persistence. A long-running job can resume from its last checkpoint instead of restarting after a failure. I use the OpenAI Agents SDK for specialist handoffs, agents as tools, session memory, guardrails and tracing. I use Google ADK with Gemini and Vertex AI when the system must operate inside a client’s Google Cloud environment. A system may include a coordinator, parallel specialists, a reducer that resolves conflicting findings, a citation verifier and a human-review gate for low-confidence decisions. 📚 ENTERPRISE RAG & DOCUMENT INTELLIGENCE I build source-grounded systems over policies, presentations, contracts, technical submissions and scanned files. Retrieval pipelines can include layout-aware OCR, structure-aware chunking, dense embeddings, BM25, Reciprocal Rank Fusion, cross-encoder reranking, page-level citations and abstention when evidence is weak. Hybrid retrieval finds both semantic meaning and exact identifiers such as clause numbers, batch numbers, product codes and case IDs that pure vector search can miss. 🚀 PRIVATE LLM DEPLOYMENT & OPTIMIZATION I deploy LLMs through managed providers, private cloud and self-hosted GPU infrastructure. This includes vLLM on Kubernetes, OpenAI-compatible endpoints, model routing, streaming, batching, asynchronous processing, retries, circuit breakers, caching and token, latency and cost monitoring. I select models based on measured quality, privacy, latency and cost instead of using the largest model for every task. 🛡️ EVALUATION, GUARDRAILS & RELIABILITY I make AI applications measurable and reliable through: • Failure taxonomies built from production traces • Golden datasets with expected answers and source spans • Retrieval, groundedness and citation evaluation • CI regression tests and structured-output validation • Hallucination, contradiction and prompt-injection testing • Repair, retry, fallback, escalation and safe-refusal paths • Token, cost and latency dashboards 📌 SELECTED EXPERIENCE REGULATED DOCUMENT VERIFICATION Built a hybrid retrieval system combining OCR, layout parsing, dense search, BM25, Reciprocal Rank Fusion and reranking. LangGraph agents reviewed technical submissions in parallel and returned evidence-backed findings. A private 32B model ran on Kubernetes through vLLM because documents could not leave the network. ENTERPRISE GOVERNANCE INTAKE Developed a FastAPI and LangGraph platform that checks submissions against internal standards and returns structured findings with evidence. It includes persistent state, parallel review, retries, escalation, human checkpoints, multimodal analysis and pluggable guardrails. 🔌 WORKFLOW & SYSTEM INTEGRATION When an AI system needs Gmail, Google Calendar, Drive, Slack, CRM or internal-system connectivity, I use APIs, webhooks, n8n or custom Python services. n8n is an optional integration layer. Critical reasoning and validation remain in testable, version-controlled Python. STACK Python, FastAPI, LangGraph, OpenAI Agents SDK, Google ADK, Gemini, Claude, Azure OpenAI, n8n, PostgreSQL, pgvector, Redis, Docker, Kubernetes, vLLM, Langfuse, OpenTelemetry, AWS, Azure, GCP and Vertex AI. Send me what is slow, unreliable or difficult to scale. I will respond with a practical architecture, an honest estimate and a clear recommendation.