Headline

AI Architect | Data Science Manager | GenAI, LLMs, MLOps, LLMOps | Enterprise AI Platforms | Azure | NLP & Computer Vision | Team Builder

About

I work at the intersection of AI, platforms, and enterprise decision-making. Over the last decade, I’ve led the design and scale-up of AI systems across classical ML and Generative AI - moving organizations from fragmented POCs to reliable, governed, production-grade AI platforms. My focus areas include: • Scaling GenAI and ML systems beyond experimentation • LLMOps, MLOps evaluation, monitoring, and cost control • AI governance, security, and enterprise readiness • Translating AI trade-offs into business decisions for senior stakeholders I enjoy solving the problems that surface only when AI meets real-world constraints—complex organizations, legacy systems, cost pressure, and accountability. Always interested in conversations around enterprise AI scale, platform design, and responsible adoption.

Location

Bengaluru

AI stack

anthropic apis

langchain

langfuse

langgraph

ollama

huggingface

Work experience

Ai Engineering manager at ZS Associates, Feb 2026 - Present · 8 mo, Noida

Data Science Manager at Wesco Anixter, Jun 2023 - Feb 2026 · 2 y 8 mo, Bengaluru, Karnataka, India

Lead Data Scientist at Yum! Digital & Tech India, Aug 2022 - Jun 2023 · 11 mos, Gurugram, Haryana, India

Senior Data Scientist at Bosch, Jul 2019 - Aug 2022 · 3 yrs 2 mos, Bengaluru, Karnataka, India, I focused on delivering innovative data-driven solutions that added significant value to our customers. • Developed applications using Python frameworks to visualize critical data aspects with Power BI. • Analyzed customer data to uncover insights supporting business initiatives and decision-making. • Created models for lane and object detection, contributing to advancements in autonomous vehicle technology.

Research Intern at Symbiosis Center for Medical Image Analysis, Mar 2018 - Jun 2018 · 4 mos, Pune/Pimpri-Chinchwad Area, Proposed a Deep learning model that automates the identification of tumor segments and superimposes the output on original scans which clearly identifies all the segments. The model was equipped with new activation function and ‘squeeze and excitation block’ which enhanced the model output which was validated using similarity index. The model was trained on AWS Deep Learning instances, sponsored by SCMIA. The future score of the proposed model, can be used in building an application which would help Neuro-Surgeons/Radiologist to identify and operate on tumors more precisely. The model was prepared using python libraries which can be easily deployed.

Design Engineer at Jacobs, Mar 2014 - May 2017 · 3 yrs 3 mos, Delhi, • Conducted gap analysis between proposed design and current site implementation using Python. Conducted weekly updates and client requirements during different phases of the project which included summary reports and dashboard preparation using MS Excel for further analysis.

Education

Master of Business Administration - MBA, Data science and Data analytics, SCIT - Symbiosis Centre For Information Technology

Bachelor of Engineering (BE), Electrical, Electronics and Communications Engineering, Birla Institute of Technology and Science, Pilani