Shrey Tripathi

Bio

I work on foundation models and the research and engineering involved in developing them. At ZenteiQ, I currently lead post-training work within the Dhīr foundation-model program. I work across the model-development process, with a particular focus on supervised fine-tuning, reinforcement learning, evaluation, and the engineering needed to move experiments toward release. Our team is preparing its first 2B and 5B model releases. My broader research interests span pre-training, data, alignment, and efficient training systems. I am interested in how choices across the model-development process shape capabilities, reliability, and behavior.

My post-training work includes instruction and conversation data, tool-use behavior, synthetic-data workflows, and human-preference collection. Our team has completed initial GRPO experiments, and I am planning further preference-optimization work. I am particularly interested in connecting training signals to measurable changes in model behavior, including multilingual and India-relevant use cases.

Model safety is part of that work. I contribute to safety-data design, red-teaming, evaluation planning, and the safety section of our release documentation. This includes examining harmful responses, unnecessary refusals, jailbreak resistance, language coverage, and the limits of the evidence available for a particular checkpoint.

I also work on the systems that support research and deployment. This includes our internal data-generation and post-training platform, GPU and TPU inference with vLLM, Kubernetes-based operations, and deployment workflows. My earlier platform work at ZenteiQ spans Python and Go services, shared libraries, API gateways, authentication, event-driven integration, databases, CI/CD, and observability. I have implemented model-experiment controls and turn tracing, inference gateway components, Go data services, and reusable deployment tooling. I contribute to technical planning, code review, hiring, and coordination across research and engineering.

I joined ZenteiQ in June 2024. ZenteiQ builds physics-native scientific foundation models, pre-trained from scratch, which began at IISc Bangalore and are backed by the IndiaAI Mission. From June 2024 to March 2025, I was also a Project Associate at IISc’s AiREX Lab. Before ZenteiQ, I was a backend engineering intern at Contlo, where I moved campaign delivery from a Rails monolith to Kafka-based microservices. Earlier, I worked on Simulink Model Advisor reporting at MathWorks, explored sparse neural-network inference using GPUs at IIT Madras, and contributed to research on automated application processing at IIIT Bangalore. I graduated from IIIT Bangalore in 2024 with an Integrated M.Tech. in Computer Science and Engineering.

I want to contribute across the development of foundation models, from the data and objectives used in pre-training to adaptation, alignment, and evaluation. My current post-training and systems work gives me a practical perspective on how those choices interact.

For research and engineering conversations, you can email me at shreytripathi27 [AT] gmail.com or find me on LinkedIn and GitHub.

Focus Areas

Research interests: pre-training, data, alignment, and efficient model training; how data, learning objectives, and scalable systems shape model capabilities and behavior.

Model training and alignment. My current work centers on supervised fine-tuning, reinforcement learning, and preference-data workflows. My broader research interests include pre-training and the interaction between training stages, model capabilities, and behavior.

Data and evaluation. I contribute to synthetic-data and human-feedback workflows, safety-data design, red-teaming, multilingual evaluation, and release documentation. I focus on understanding failures and making evaluation evidence useful for model-development decisions.

ML systems and infrastructure. I work on systems supporting model experimentation and GPU/TPU inference, including inference gateways, generation controls, model and tool-call tracing, vLLM, Kubernetes, and deployment workflows. I am interested in scalable training systems and efficient experimentation.

Platforms and technical leadership. My platform work spans Python and Go services, API gateways, data workflows, databases, CI/CD, and developer tooling. I also contribute to technical planning, code review, hiring, and coordination across teams.

Selected Work

Education, Recognition & Skills

Integrated M.Tech. in Computer Science and Engineering, 2019–2024
International Institute of Information Technology Bangalore

Smart India Hackathon 2022 winner. Member of a winning team working on numerical weather forecast bias correction. IIIT Bangalore report
GATE 2022, All India Rank 2899. LiFT Scholarship awardee (The Linux Foundation), 2021. NTSE Scholarship awardee (NCERT), 2017.

Open source. Merged pull requests to Open Library (Internet Archive), TWLight (Wikipedia Library), and Mathesar.

Technical skills.
Languages: Python, C, C++, Java, Go, SQL
ML and serving: CUDA, PyTorch, Milvus, llm-d, KV-cache-aware routing, TPU inference, FastAPI
Infrastructure: Kafka, Temporal, Kubernetes, GKE, Helm, Terraform, Redis, Docker, GitHub Actions
Databases: PostgreSQL, Cassandra, YugabyteDB, ScyllaDB, MongoDB

Earlier Projects

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• All

Swasthya Suraksha: A streamlined ABDM framework
Seamless exchange of medical records across establishments

CryptoWill
Your digital will for cryptocurrencies

Pagerank
Rank web pages by importance

Depocalypse
An integrated decentralized NFT marketplace

Minesweeper
An AI that plays minesweeper

Cache
Implementation of a DM Cache and a 4-way SA Cache

IAS Architecture
Implementation of the IAS(Princeton) architecture

Tic Tac Toe
A tic-tac-toe AI using the minimax algorithm

just-post-it
A twitter-like social network

Swasthya Suraksha: A streamlined ABDM framework
Seamless exchange of medical records across establishments

AskReddit troll Question Detection
Detection of troll questions on r/AskReddit, a popular subreddit

Bias Correction in NWP model
Prediction and visualization of bias-corrected temperature from the Numerical Weather Prediction (NWP) model

CryptoWill
Your digital will for cryptocurrencies

Pagerank
Rank web pages by importance

Depocalypse
An integrated decentralized NFT marketplace

Minesweeper
An AI that plays minesweeper

Cache
Implementation of a DM Cache and a 4-way SA Cache

IAS Architecture
Implementation of the IAS(Princeton) architecture

Image Modifier
Convert a .ppm image from RGB to BRG

Package Shipment Management
Modelling Package Shipment Management in Swing

Game Show
A game show using sockets

Tic Tac Toe
A tic-tac-toe AI using the minimax algorithm

Hostel Management System
C implementation of a Hostel Management System

DAA - Final Project
Final project for CS-202 Design and Analysis of Algorithms

just-post-it
A twitter-like social network

reviewyourreads
A book review application

Degrees of Separation
Determine the degrees of separation of two actors

Wiki
A Wikipedia-like online encyclopedia

Flatroom
An IRC-style chat application using sockets

savetimewithus
A todo list with a twist

Navigation System
Modelling roads, intersections and routes