Headline

Databricks Gen-Ai certified | GenAI Engineer | LangGraph • Azure OpenAI • LLM Agets • RAG | Graph Analytics • FastAPI | Open to AI Enineer Roles

About

I'm a GenAI Engineer with 3+ years of experience designing and deploying AI systems — currently working at LatentView Analytics on Microsoft-facing engagements where I build production-grade Agentic AI pipelines. My core work centers on LangGraph-powered multi-agent workflows, Azure OpenAI integrations, Retrieval-Augmented Generation (RAG) systems, and FastAPI-based backend services. I've built AI agents that automate frontier research tasks, assist in sales forecasting, and analyze graph-structured data at scale — combining graph analytics with LLM reasoning to surface insights that conventional AI pipelines miss. Before moving into GenAI, I spent nearly two years at Transpoze Labs as a Machine Learning Engineer, where I optimized computer vision models (OpenVINO), achieving 3–4× inference speedups across face detection, license plate recognition, and people-count tracking. This systems-level ML background directly informs how I approach production GenAI — I don't just prototype; I build for reliability, latency, and scale. Earlier, I worked as a Data Scientist at TuringMinds.ai, where I developed ML forecasting models and gained hands-on experience with end-to-end model pipelines. I hold a Databricks Certified Generative AI Engineer Associate certification and a PGP in Data Science from INSOFE. Tech stack: LangGraph • Azure OpenAI • RAG • FastAPI • Python • Azure Blob Storage • Azure SQL • Graph Analytics • LLMs • Agentic AI Currently targeting: GenAI Engineer | AI Agent Engineer | LLM Engineer | Azure OpenAI Developer | Applied AI Engineer Open to full-time and contract roles | Hyderabad / Bangalore / Remote

Location

Hyderabad

AI stack

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codex

cursor

github copilot

hermes agent

Work experience

Senior Analyst – GenAI Engineer at LatentView Analytics, Jun 2025 - Present · 1 yr 5 mos, Banglore, Client: Microsoft | GenAI & Agentic AI Engineering • Designed and deployed multi-agent agentic AI workflows using LangGraph and Azure OpenAI, enabling automated research, graph-driven analysis, and conversational AI capabilities for Microsoft-client projects. • Built production RAG (Retrieval-Augmented Generation) pipelines integrating graph analytics, Azure Blob Storage, and Azure SQL — enabling LLM-powered insights over structured and semi-structured enterprise data. • Developed and maintained FastAPI-based backend services to expose AI agent capabilities as scalable REST APIs, supporting downstream integration with client systems. • Engineered AI agents including a Frontier Research Agent, an AMA AI Messaging Assistant, and a Cricket Analyst Agent — each combining tool-calling, memory management, and multi-step LLM reasoning via LangGraph. • Contributed to an AI Classroom Analytics Platform and AI Sales Forecasting Platform, applying GenAI and ML approaches to educational and business forecasting use cases. • Secured Second Runner-Up at the LatentView Analytics AI SWAT Hackathon 2026, demonstrating applied innovation in GenAI systems under competitive conditions.

Machine Learning Engineer at Transpoze, Aug 2023 - Jun 2025 · 1 yr 11 mos, Hyderabad, • Spearheaded optimization of computer vision inference pipelines using Intel OpenVINO, achieving 3–4× inference speedups across face detection, face recognition, license plate recognition (LPR), and people-count tracking applications. • Developed and maintained production-grade CV models for real-time edge deployment, ensuring low-latency performance across embedded and server-side environments. • Collaborated with cross-functional engineering teams to integrate ML model outputs into product features, bridging the gap between model development and application delivery. • Designed data pipelines and preprocessing workflows to support model training and evaluation for computer vision tasks at scale. • Applied model evaluation, A/B testing, and performance benchmarking practices to continuously improve model accuracy and inference efficiency in production.

ML data associate - 1 (LLM) at Amazon, May 2023 - Jul 2023 · 3 mos, Hyderabad, Telangana, India, The Alexa Data Services (ADS) organization provides data creation, curation, and analytics services to help develop, test, and train Alexa AI. We work closely with Amazon Machine Learning (ML) Science and Modelling teams, providing the critical data they need to improve Alexa’s speech recognition and language understanding capabilities

Data Science Intern at The Sparks Foundation, Jan 2023 - May 2023 · 5 mos, Hyderabad, Telangana, India

Data scientist Intern at Feynn Labs, Dec 2022 - May 2023 · 6 mos, Hyderabad, Telangana, India, As an intern, I was responsible for the planning, organizing, and directing as well as managing and a thorough evaluation on a daily basis of the tasks within the organization/company. I was assigned following projects: AI in Education: Projects under Development: Smart Schools, FeynnAI: Learning Assistant, Adaptive Question System, Segmentation of Foundational Knowledge Blocks. AI in Economy: Product/Service: Predictive Market Analysis, Market Analysis and Segmentation. Projects under Development: Product Segmentation, Micro-Macro Economic Forecasting, Consumer Spending Behaviour Prediction System. AI in Innovation: Projects under Development: Feynn Labs AI Incubator. In addition, I was given exposure through the management and technical tasks to help you upskill yourself and build my resume.

Education

Postgraduate program, data scientist, International School of Engineering (INSOFE)

Bachelor of Technology - BTech, Civil Engineering, SRKR Engineering College

Public links

github: https://github.com/sanju0392