Headline

Applied AI Engineer @Netscribes

About

Applied AI Engineer building production LLM systems, RAG pipelines, and agentic workflows for enterprise use cases. I work across Python, FastAPI, LangChain, tool orchestration, evaluation design, and observability to build AI systems that are reliable in real workflows, not just impressive in demos. My recent work has focused on enterprise search and retrieval systems, meeting intelligence workflows, structured extraction, and API-integrated AI products with measurable gains in quality, latency, and reliability. I care most about shipping systems that are useful, debuggable, and ready for production. I’m especially interested in roles where I can build agents, orchestration layers, eval harnesses, and backend systems that help AI operate inside real organizations.

Location

Bengaluru

AI stack

anthropic apis

claude code

grok

langgraph

ollama

DSPy

Work experience

Data Analyst & AI Engineer at Netscribes, Feb 2024 - Present · 2 yrs 8 mos, Bengaluru, Karnataka, India, I build and operate production AI systems for enterprise use cases, with a focus on retrieval, orchestration, and reliability. My work includes architecting Azure AI chatbots over large-scale enterprise knowledge bases, improving retrieval relevance, optimizing latency, and delivering responsive experiences for business users. I’ve built FastAPI-based services for ingestion, retrieval, and LLM orchestration, and I work closely with stakeholders to improve answer quality, search behavior, and deployment readiness in real-world workflows. I enjoy designing AI systems that balance performance, accuracy, and operational reliability while remaining scalable, maintainable, and practical for enterprise adoption. I built a meeting intelligence platform that transcribes conversations, generates summaries, and extracts structured action items, owners, and deadlines from recorded meetings. For this workflow, I designed evaluation pipelines to compare model performance, improve summary and action-item quality, and establish clear quality benchmarks across the system. I also implemented end-to-end observability using Langfuse to monitor latency, cost, and response quality, enabling continuous improvement and reliable operation in production environments. I enjoy building AI systems that are not only accurate, but also dependable, debuggable, and useful in real-world settings.

Data Science Intern at GenieTalk Private Limited, Sep 2023 - Dec 2023 · 4 mos, Indore, Madhya Pradesh, India, Worked on improving the NER of the model used.

Education

Bachelor of Technology - Btech, Computer Science with specialization in Data Science, Medicaps university

Sri Sathya Sai Vidya Vihar High School

Public links

github: https://github.com/architkuiya