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AI Engineer with 1+ years in Python & LLM-powered applications
GenAI Developer with 1 year of hands-on experience in designing and deploying LLM-powered applications and AI agents. Proficient in Python, SQL, and experienced in building agent-based workflows using LangChain and MCP. Strong hands-on exposure to Transformers, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) pipelines. Skilled in prompt engineering to improve reasoning, task execution, and response quality in LLM-driven systems. Experienced in developing scalable AI/ML APIs and backend services using FastAPI for production-ready GenAI applications.
Jawaharlal Nehru Technological University Kakinada (JNTUK)
B.Tech. · EEE
May 1, 2019 – June 1, 2022
GenAiLakes
Gen AI Developer
May 1, 2025 – Present
Hyderābād, Telangana, India
AgriHub – AI-driven Agriculture Management Platform
May 1, 2025 – June 1, 2026
Architected and implemented a multi-agent GenAI platform (AgriPilot) using LangChain, LangGraph, and MCP, enabling domain-specific AI agents for irrigation, nutrient management, pest detection, and harvest prediction. Built LLM-powered advisory agents hosted via vLLM/TGI, integrating RAG pipelines with pgvector/Qdrant/ChromaDB to retrieve crop SOPs, soil norms, and agronomy guidelines. Built and optimized RAG pipelines using LangChain retrieval chains, embeddings, and vector databases (pgvector/Qdrant/ChromaDB) for domain-grounded LLM reasoning. Developed scalable AI inference APIs using FastAPI, exposing LLM reasoning, prediction models, and agent workflows as REST endpoints for web/mobile integration. Integrated Guardrails.ai / NeMo Guardrails to enforce policy compliance, fertilizer dosage limits, pesticide safety constraints, and hallucination control in LLM outputs. Integrating real-time data streaming pipelines, enabling low-latency processing and high-throughput ML/LLM workflows. Designed vector-based memory architecture using pgvector/Qdrant to store irrigation logs, soil health cards, outbreak history, and crop-specific embeddings for contextual grounding. Designed dynamic agent routing and orchestration using MCP (Model Context Protocol) to enable tool invocation, context passing, and modular agent communication. Applied advanced prompt engineering techniques, including few-shot prompting, role-based prompting, structured output prompting (JSON schema), and chain-of-thought reasoning to improve accuracy and reduce hallucinations. Applied modular Python architecture and OOP principles to create maintainable, extensible, and production-ready Agentic AI systems. Utilized LangSmith for LLM debugging, trace analysis, latency monitoring, and evaluation of prompt performance across multi-agent workflows. Integrated Langfuse for observability, token tracking, cost monitoring, and production-grade telemetry of LLM interactions. Designed visual agent workflows and rapid prototypes using LangFlow, accelerating experimentation and testing of multi-step reasoning pipelines. Implemented experiment tracking and observability using MLflow, logging model parameters, irrigation metrics, nutrient efficiency, pest detection accuracy, and harvest prediction outcomes.
Cultural Fit Analysis
The candidate's single, highly detailed project, AgriHub, demonstrates deep engagement and ownership, which aligns well with a culture that values initiative and comprehensive problem-solving. The project's focus on an AI-driven agriculture management platform shows an interest in applying AI to real-world, impactful domains. The breadth of technologies used within this single project (LangChain, LangGraph, MCP, RAG, FastAPI, vLLM, Guardrails.ai, LangSmith, Langfuse, MLflow, various vector databases) indicates a strong drive for continuous learning and adoption of new tools, which is a positive cultural fit for innovative tech environments. The role as 'Gen AI Developer' at 'GenAiLakes' directly aligns with the target 'AI Engineer' role, suggesting a clear career path and interest.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong focus on problem-solving, system architecture, and operational efficiency (e.g., improving deployment speed, reducing provisioning time, increasing model uptime). The detailed explanation of the AgriHub project suggests an ability to work on complex, multi-faceted problems and deliver end-to-end solutions. The mention of 'Cross-Functional Team Leadership' in core competencies, though not explicitly detailed in experience, indicates an awareness of collaborative environments. The candidate appears to be a motivated individual keen on applying cutting-edge technologies.