Laukik Bhalchandra Nakhwa

I build AI systems that bridge the gap between experimental research and industrial-scale production. My work is focused on making Machine Learning operationally resilient—whether that means reducing deployment cycles for Pfizer’s global supply chain by 98% at ZS, or developing robust control policies for legged robots to close the sim-to-real gap.

Currently, I am an Advanced Data Science Associate at ZS, where I architect MLOps pipelines using FastAPI and Kubernetes. I also continue to collaborate remotely as a Research Scholar with the National University of Singapore (MARMot Lab), specializing in Deep Reinforcement Learning under Prof. Guillaume A. Sartoretti.

I completed my undergraduate at BITS Pilani (Goa) pursuing a dual degree: a B.E. in Electronics and Instrumentation Engineering and an M.Sc. in Biological Sciences.

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Experience & Research Focus

• Global Scale ML & MLOps: At ZS, I developed and deployed XGBoost and KNN-based predictive models for Pfizer’s worldwide supply chain. These models predict critical SCM parameters—such as lead-time and shelf-life—directly impacting global inventory optimization. To support these models, I engineered a modular pipeline using FastAPI and Kubernetes that reduced deployment latency by 98% (4 hours to 5 minutes), integrated with Snowflake for TB-scale data processing and 3-tier drift detection.
• Legged Locomotion (Remote Collaboration): Following my on-site research at MARMot Lab (NUS), I continue to contribute to imitation and curriculum learning frameworks. I co-developed APEX, a framework for robust motion tracking on legged robots, featured at the 21st Dynamic Walking Conference (Germany).
• Past Collaborations:
  • Medical AI: Worked with Prof. Meenal Kowshik on predicting Autoimmune diseases via genetic correlation.
  • Computer Vision: Developed human pose detection/correction systems with Prof. Kunal Korgaonkar at the CSIS Robotics Lab.
  • Industrial IoT: Interned at Pyrotech Electronics to build an E-Medical Kiosk for mass healthcare dispensing.

My passion lies in applying theoretical advancements in Learning to meaningful, physical-world tasks. When I am not optimizing global models or collaborating on research, I love to play football, practice martial arts, and enjoy watching live sports.