01 · Agent
GitHub SWE Agent
LLM harness with agentic loops, GitHub API integration, webhook pipelines, planning/reasoning/execution layers and sandboxed tool-calling for issue-to-PR automation.
Founding AI Engineer
4+ years building AI-powered products from idea to production — spanning agents, SWE agents, RAG, conversational AI, automation and MCP.
AI-native product builder with depth across agents, retrieval, backend systems, automation and user-facing applications.
AI is used across the full product loop — reasoning, retrieval, tool use, backend services and user-facing applications.
AI engineering + backend + product execution. Strongest when the problem is ambiguous and the system still needs to ship.
Best suited to ambiguous environments where someone needs to design the architecture, build the system and keep pushing it toward production quality.
Core: multi-agent systems, planning, reasoning, execution, tool-calling and agentic loops.
Built: OpenClaw-based GitHub SWE agent for issue-to-PR automation plus documentation automation.
Built: semantic retrieval, dynamic context pipelines, hallucination control and response validation.
Built: audit agents, rule-based validation, guardrails, evaluation and observability systems.
Stack: FastAPI, Node.js, REST APIs, PostgreSQL, MongoDB, Redis and real-time inference.
Stack: React, Next.js, Tauri and Capacitor across web, desktop and mobile experiences.
Built production AI systems and REST APIs; designed multi-agent systems; developed an OpenClaw GitHub SWE agent; built webhook/tool pipelines and sandboxed execution; optimized inference for cost, latency and scale; implemented RAG.
Designed AI agents and audit systems for workflow/code validation, built LLM + rule-based validation pipelines with guardrails, prototyped decentralized AI data systems and agent workflows.
Architected AI applications integrating LLMs into production workflows, built FastAPI and Node.js backends for real-time inference, and developed frontend systems with Next.js and React.
Built intelligent agents using LangChain, Langflow and PyTorch; developed RAG systems with semantic search and dynamic context retrieval; implemented hallucination control and response validation.
Built data-management applications and ETL pipelines for blockchain wallet and transaction data, including extraction, transformation, reporting and analytics workflows.
Worked on order-management systems, APIs, data analysis, ETL pipelines and operational reporting/automation workflows.
LLM harness with agentic loops, GitHub API integration, webhook pipelines, planning/reasoning/execution layers and sandboxed tool-calling for issue-to-PR automation.
Cross-platform workforce and organization platform with embedded AI assistants, shared React/TypeScript codebase, Tauri desktop apps and Capacitor Android apps.
Python audit agent using LLMs and rule-based validation, with traceability, explainability, evaluation/observability and RAG-based real-time context.
Generates structured documentation from codebases by analyzing APIs, architecture and dependencies for context-aware output.
Semantic-search and dynamic-context retrieval systems with response validation and hallucination-control patterns for production workflows.
AI-powered applications combining real-time inference, FastAPI/Node.js backends and React/Next.js interfaces for production use.
Hands-on AI product ownership, fast iteration, system design and production execution — without separating “AI work” from the rest of the product.
BTech, Data Science — TKR College of Engineering & Technology, Hyderabad.
Certifications: Microsoft Azure Fundamentals, Python for Data Science and AI, NPTEL Data Structures & Algorithms, HackerRank certifications.
Additional foundation: Diploma in Mechanical Engineering.