Name: Chetan Virani
Profile: AI / GenAI Engineer
Email: viranichetan666@gmail.com
Phone: (982) 553-1581
Skills
LangChain, LangGraph, RAG, Prompt Engineering, MCPAbout me
10+ Years experienced engineer now focused on building production LLM and agentic AI systems on top of modern data platforms, with a strong complementary background in full-stack and data engineering. Targeting roles in innovative tech companies to contribute to AI product development and platform engineering.
✳️ Generative AI & LLM Engineering
LANGCHAIN, LANGGRAPH, RAG, EMBEDDINGS & VECTOR SEARCH, PROMPT ENGINEERING, FUNCTION CALLING, LLM EVALUATION, AWS BEDROCK, ANTHROPIC CLAUDE API, OPENAI API, GOOGLE GEMINI
✳️ Agentic AI & Orchestration
MULTI-AGENT SYSTEMS, TOOL USE & FUNCTION CALLING, MODEL CONTEXT PROTOCOL (MCP), HUMAN-IN-THE-LOOP REVIEW, STATEFUL GRAPH-BASED AGENTS, N8N WORKFLOW AUTOMATION
✳️ Data & Analytics Engineering
PYTHON, DBT, DAGSTER, TRINO / STARBURST, SNOWFLAKE, SQL, ELT / ETL PIPELINES, DIMENSIONAL MODELLING
✳️ Frontend Development
HTML5, CSS3, TAILWIND CSS, BOOTSTRAP, JAVASCRIPT, TYPESCRIPT, REACT JS, NEXT JS, VUE JS, ANGULAR
✳️ Backend Development
NODE JS, EXPRESS JS, NEST JS, PYTHON (FASTAPI), GO, REST APIS, GRAPHQL, MICROSERVICES, SERVERLESS
✳️ Database & Cloud
POSTGRESQL, MYSQL, MONGODB, REDIS, SNOWFLAKE, PGVECTOR, AWS, DOCKER
Specialties:
- An AI / GenAI Engineer with over 10 years of software engineering experience, now focused on building production LLM and agentic AI systems on modern data platforms.
- Design and ship multi-agent workflows with LangChain and LangGraph, Retrieval-Augmented Generation (RAG) pipelines, and Model Context Protocol (MCP) servers that give domain-specific AI assistants tool access and persistent context across systems.
- Complementary strength in Python data engineering — dbt, Dagster, Trino/Starburst, and Snowflake — so AI features stay grounded in reliable, well-modelled data.
- Proven full-stack engineer across the MERN and MEAN stacks (React, Next.js, Angular, Vue, Node.js, NestJS, GraphQL), with a record of architecting greenfield products end to end.
- Applies advanced prompt engineering and evaluation practices — structured outputs, few-shot and chain-of-thought patterns, and regression testing over prompt changes — to improve accuracy and reduce token spend.
- Embeds AI into the software development lifecycle with AI-assisted coding tools for code review, test generation, and documentation to shorten delivery cycles.
- Proven track record of collaborating with cross-functional teams, including designers, engineers, and product teams, to deliver high-quality software solutions.
- Key strengths include performance optimization, comprehensive code reviews, and mentoring team members to foster a culture of continuous learning and improvement.
- As a proactive professional, excels in proposing improvements, influencing technical decisions, and driving projects to successful completion.
