Vinay Sawant


Core Distributed Protocol Engineer (Rust)
Mumbai City, IN
[email protected]

Github: vinay10949

About Me

Dynamic Blockchain & Backend Protocol Engineer with 11+ years of experience building high-performance distributed systems and secure backend architectures in Rust and Go. Proven expertise in MPC, consensus, and threshold signature protocols driving scalable Layer-1 and Layer-2 networks. Passionate about advancing decentralized and verifiable computation through innovation in ZK, EigenLayer, and Bitcoin-native systems.


Core Competencies: 

Languages: Rust, Go, Python, Solidity, Java, NodeJS, Zig

Protocols: MPC, DKG, Threshold Signatures, Consensus Design, BFT, ZKPs, Bitcoin Script, EigenLayer AVS

Backend Expertise: Distributed Microservices, High-QPS APIs, Event-Driven Architectures, gRPC, Prometheus, Docker Domains: Blockchain Protocols, Cryptography, Fintech Systems, Zero-Knowledge Systems, Smart Contracts.

Cloud : AWS, GCP

Blockchain: Ethereum, Bitcoin, Solana

Work Experience


Rust Engineer - Ducat Protocol (June 2024-Oct 2025)

Focus: Bitcoin-native DeFi, MPC Guardian Network, Threshold Cryptography
  • Architected and implemented a decentralized MPC Guardian Network for Bitcoin-native DeFi, enabling scriptless multisig enforcement and zero custodial trust.
  • Integrated FROST Threshold Signatures for secure, efficient, and censorship-resistant transaction co-signing (PSBTs)
  • Built a distributed verification layer for PSBTs to enforce overcollateralization and liquidation rules without custodial control.
  • Engineered on-chain data integrity using OP_RETURN encoding for transparent, auditable, and upgradeable vault states.
  • Developed multi-layer liquidation safeguards, including MPC oversight and oracle blindness, to mitigate manipulation risks.


Lead Distributed Systems Engineer - Holonym (April 2023-April 2024)

  • Engineered "Human Keys" a privacy-preserving system to derive cryptographic keys from human attributes for secure, nullifier-based ZK Identities. 
  • Designed and implemented advanced cryptographic protocols for decentralized systems, enabling secure key generation and provable encryption via DKG.
  • Designed and implemented a protocol that allows elliptic curve point multiplication using a distributed private key, ensuring confidentiality and security.
  • Leveraged EigenLayer and Symbiotic to deploy the protocol as an AVS , enhancing shared security.
  • Used FSPKE (Forward Secrecy Public Key Encryption) to encrypt key shares per epoch.

Technology: RUST, Tauri,Cryptography, Zero-Knowledge Proofs, Distributed Systems, Smart Contracts


Core Distributed Systems Engineer-Versatus Labs (August 2021-April 2023)

  • Drove L1 Blockchain development focused on lightweight consensus, implementing DKG, Threshold Signatures, and Quorum Election protocols.
  • Designed a Left-Right Concurrency Model over Mempool, enabling efficient broadcast using RaptorQ over UDP for the networking stack.
  • Led the creation of a Decentralized Task Scheduler, optimizing network utilization via the FarmerHarvester Quorum model. 
  • Contributed to the Lasr L2 Chain, building a custom AVS Verifier for EigenLayer and writing Staking Contracts.
  •  Integrated metrics using Prometheus and developed the storage layer for IPFS.
  • Some of my contributions: https://versatus.io/library/farmer-harvester-bft.pdf

Technology: RUST


 Rust Engineer -SupraOracles ( July 2021 -July 2022) 

  • Collaborated with PhDs to develop a novel Hybrid Consensus (Proof of DTS) mechanism for a Layer 1 blockchain, optimizing for high throughput and low latency.. 
  • Designed and implemented critical consensus modules, including nested DKG, the Tribe Clan Model, Threshold Signer, and BLS Sig Aggregator. 
  • Played a key role in optimizing performance and scalability of the consensus algorithm through rigorous testing, debugging, and architectural contributions.
  • Mentored junior engineers and actively participated in code reviews to ensure reliability and best practices.

Technology: RUST


 Senior Architect | SuperMoney (June 2016 – 2020)

  • Spearheaded technical architecture for Fintech services, designing and deploying a high-performance Microservices backend with REST/gRPC APIs. 
  • Implemented a calibrated ML default prediction model that reduced loan default rates by 40%. 
  • Developed a real-time notification engine for customer engagement and built deep learning models for document identification/OCR (PAN, Aadhar, Insurance), alongside an SMS text analytical engine for expense and cash flow profiling.

Software Engineer(ML/Data Science) | BookMyShow (Aug 2014 – May 2016)

  •  Architected and deployed a user-centric Music Recommendation Engine, successfully solving the Cold Start challenge and achieving real-time recommendations for 50 million+ users
  •  Engineered a high-throughput Seat Occupancy module that pinged 5,000+ cinemas in real-time, effectively resolving race conditions during peak booking seasons using Node.js, RabbitMQ, and Golang.

Blockchain (2020-2025)

Human Network: Privacy-Preserving Distributed Identity and Encryption Protocols

Objective: Developed a decentralized cryptographic identity system leveraging human attributes, provable encryption, and distributed key generation to power secure ZK-authentication and undercollateralized DeFi.
  • Human Keys: Designed a privacy-preserving system that derives cryptographic keys from human traits, enabling nullifier-based authentication and identity reuse without revealing user data.
  • Distributed Private Key Computation: Engineered a protocol to perform elliptic curve point multiplication using a distributed private key, preserving key secrecy even across untrusted nodes.
  • Provable Encryption & Conditional Decryption: Built a ZK-compatible protocol allowing users to prove encryption correctness and enforce decryption conditions via smart contracts, unlocking secure on-chain identity and DeFi interactions.
  • Forward Secrecy Encryption (FSPKE): Encrypted key shares per epoch, ensuring forward secrecy across distributed nodes in a rapidly evolving network.
  • Network Optimization: Co-architected and optimized the distributed node infrastructure, enabling efficient key share propagation and low-latency request handling at scale.
  •  AVS Deployment & Shared Security: Integrated with EigenLayer and Symbiotic to deploy the protocol as an Actively Validated Service (AVS), leveraging shared security across Ethereum-based systems. 

Technology: Rust, Cryptography, Distributed Systems, ZK Proofs, Smart Contracts, EigenLayer, AVS, FSPKE 

Versatus Layer 1: Lightweight Consensus and Decentralized Scheduling for Blockchain Scalability

Objective: Architecting a high-performance Layer 1 blockchain by innovating around consensus, decentralized scheduling, and networking to optimize throughput and resilience.
  • Designed and implemented a novel lightweight consensus mechanism integrating DKG, Threshold Signatures, and Quorum Election, optimizing for minimal computation during block validation.
  • Introduced a Left-Right Concurrency Model over the mempool to support efficient parallel transaction processing and low-latency propagation via RaptorQ over UDP. 
  • Spearheaded the creation of a Decentralized Task Scheduler, enabling load balancing through a Farmer-Harvester Quorum model for smart offloading of computation.
  • Participated in research around Byzantine fault tolerance and mitigation strategies for malicious peer activity. Contributed to token emission strategies and integrated real-time metrics tracking via Prometheus, ensuring transparent node telemetry.
  • Built IPFS-backed components for persistent decentralized storage, improving data availability and retrieval.

Technology: Rust, libp2p, RaptorQ, IPFS, Prometheus, Threshold Cryptography

LASR: Building a Scalable Layer 2 Chain and EigenLayer AVS Integration

Objective: Engineered a high-throughput Layer 2 rollup chain with secure AVS integration on EigenLayer, enabling composable staking and fast execution.
  • Contributed to the development of LASR, a performant Layer 2 chain leveraging off-chain compute and modular architecture. 
  • Built a custom AVS Verifier for EigenLayer, validating decentralized execution and enabling secure coordination with Ethereum mainnet.
  • Developed staking contracts in Solidity, governing validator bonding, rewards, and slashing mechanisms. Focused on bridging and cross-layer messaging, enabling composable interactions between L1 and L2. 
  • Explored zero-knowledge-friendly validation strategies and advanced rollup state verification techniques. Technologies: Rust, Solidity, Ethereum, AVS (EigenLayer), Layer 2, zk-primitives  .

Technology: Rust, Solidity, Ethereum, AVS (EigenLayer), Layer 2, zk-primitives .

Supra Moonshot Consensus: Unleashing a Novel Era for Blockchain Technology

Objective: Pioneering a Novel Consensus(Proof of DTS) Mechanism for Layer 1 Blockchain to Enhance Security, Speed, and Finality, Redefining the Future of Decentralized Systems.
  • Collaborating with leading PhDs in Cryptography, actively involved in implementing a highly scalable and fast consensus engine.
  • Contributed to cutting-edge developments such as Nested DKG, Tribe Clan Model for Consensus, and Bls Signatures. 
  • Took part in building L1 (Layer 1) from scratch, showcasing expertise in blockchain development and architecture.
  • This experience highlights working on groundbreaking solutions and collaborating with top experts in the field, contributing to advancements in the consensus and blockchain domain.

Technology: Rust 

ML Projects (2014-2020)

Empowering Document OCR: Harnessing Python for Accurate Text Extraction

Objective: To fetch information from documents by OCR 
  • Successfully implemented OCR on various documents like PAN, Aadhar Front, and Aadhar Back, providing accurate user-level data, including photographs. 
  • Employed progressive calibration networks to achieve rotation invariance, enabling efficient extraction of photos from the documents. 
  • Designed and developed a robust API for generating QR codes for UPI payments, facilitating seamless and secure transactions. 
  • Created an advanced API capable of measuring similarity between two names, enhancing data processing and matching capabilities.
  • Developed a reliable API that validates Aadhar numbers using the Verhoeff algorithm, ensuring data integrity and authenticity.

Deep Neural Networks for Accurate Document Detection

  • Developed a robust CNN model using transfer learning with ResNet50 to accurately identify document types such as PAN, Aadhar Front, Aadhar Back, and Cheque.
  • Extensively trained the model, fine-tuning it with additional layers to achieve high accuracy in document classification. 
  • Assumed full responsibility for model training, creation, and deployment, ensuring seamless integration into the company's document processing pipeline. 
  • Leveraged Python and the Connexion framework to create a powerful API, enabling efficient interaction with the document classification model. 
  • Implemented Docker for containerization, streamlining deployment and scaling processes, resulting in significant cost savings for the company.
  •  Notably, the successful implementation of the model and API resulted in reduced false OCR instances, contributing to substantial operational cost savings.

CardioDetect: An Innovative Approach to Cardiovascular Disease Detection

Objective: To design a system for detecting cardiovascular disease.

  • Led end-to-end deployment, testing, monitoring, and retraining efforts for a document detection project, encompassing every stage from Exploratory Data Analysis (EDA) to Model Creation and Deployment.
  •  Assumed full responsibility for the detailed EDA phase, identifying key insights and patterns to inform the subsequent model creation process. 
  • Successfully developed and deployed the document detection model, rigorously testing its performance to ensure high accuracy and reliability.
  •  Implemented robust monitoring mechanisms to continuously track the model's performance and detect any deviations or anomalies. 
  • Achieved a remarkable Recall of 82% using F2 score as the evaluation metric, showcasing the model's effectiveness in correctly identifying relevant documents. 
  • The project's GitHub repository (https://github.com/vinay10949/CVD) serves as a testament to the dedication and excellence put into this endeavor, providing valuable insights for the broader community.

DeepGuard: Empowering SmartBackground Verification with Neural Networks

Objective: To use automate the problem of background verification. 
  • Developed an advanced eye detection system by extracting eye descriptors and calculating the Eye Aspect Ratio (EAR) for consecutive frames, enabling accurate detection of blinking patterns in individuals.
  •  Designed and implemented a Face-Verification module, revolutionizing profile photo authentication by comparing them with document photos, ensuring enhanced security and identity verification.
  • Pioneered the creation of a powerful Name & Address similarity engine, utilizing cosine similarity and Levenshtein Distance to facilitate efficient data matching and verification.
  • Assumed a central role in the end-to-end development process, from detailed Exploratory Data Analysis to model creation, deployment, testing, monitoring, and retraining. 

Short-Term Loan Default Prediction for Enhanced Financial Stability

Objective: To develop a robust and predictive model capable of early identification and classification of loan defaulters, empowering financial institutions with actionable insights to mitigate risk, improve decision-making

  • Led data engineering and data pipelining efforts, ensuring seamless data processing and analysis for our customer base.
  •  Conducted detailed analysis of customer data, extracting valuable insights to inform business strategies and decision-making processes. 
  • Successfully trained and tested a predictive model to identify first-time customers at risk of defaulting on a 5000 Rupee product loan. 
  • Achieved an exceptional F-Beta Score of 0.79, showcasing the model's high accuracy and reliability in predicting loan defaults. 
  • Spearheaded a groundbreaking initiative resulting in a significant 40% drop in loan default rates, enhancing financial stability and customer trust. 

Music Recommendation Engine

Objective: Pioneering User-Centric Music Experience: Develop a Cutting-Edge Solution to Address the Cold Start Challenge, Elevate User Engagement, and Acquire Actionable User Data on the Music Application.

  • Successfully tackled the Cold Start problem and improved user engagement on a music application. 
  • Utilized Music Information Retrieval (MIR) from the Vienna University library, incorporating features like rhythm patterns and temporal descriptors (approx. 2000 dimensions per song). 
  • Implemented Locality Sensitive Hashing and the Epsilon Greedy Approach for robust music recommendations. 
  • Technology stack: Python, Protobuf, Konga, Redis. 
  • Achieved real-time music recommendations for a user base of 50 million users, solving the Cold Start challenge effectively.

BackEnd Developer Projects (2014-2020)

Kartavya: Empowering Efficiency with the Delayed Queue

Objective: Enhance Operational Efficiency through Delayed and Real-time Notifications, Enabling Seamless Delivery of Close to 100 Million Notifications Per Day.

  • Designed and developed the Delayed Queue Code Named "Kartavya," an intelligent queue system capable of enqueueing delayed actions and executing them at scheduled times.
  •  Implemented Delay push messaging for reliable and at-least-once delivery, along with a fail and retry mechanism, ensuring robustness and data integrity.
  • Developed a set of 3 REST API calls to enable seamless message pushing, querying schedules, and deleting schedules from the queue, providing efficient user interaction.
  • Leveraged cutting-edge technologies such as Redis, Golang, and AWS Cloud to build a cost-effective and highly efficient system, optimizing performance and scalability.
  • The result was a successful implementation of the system, which streamlined processes, reduced costs, and significantly increased overall efficiency.

Streamlining Video Identity Verification for Karza and Signzy

  • Developed a cutting-edge Microservice to encapsulate the business logic for Video KYC of Karza and Signzy. 
  • Leveraged Golang to create a robust and efficient solution, ensuring seamless execution of the Video KYC process.
  • Successfully integrated the Microservice with the Karza and Signzy platforms, enhancing their capabilities for identity verification and compliance. T
  • The Microservice enabled streamlined and secure Video KYC processes, providing a seamless user experience for customers.
  • Played a pivotal role in ensuring data privacy, security, and compliance with regulatory requirements throughout the Video KYC workflow.
  • This Microservice significantly contributed to improving the overall efficiency and accuracy of Video KYC operations for both Karza and Signzy, solidifying their positions as leaders in the industry.

Data Pipeline: Efficient Data Migration from AWS Athena to BigQuery 

  • Designed and constructed large-scale data pipelines for seamless data migration from AWS Athena to BigQuery, enabling efficient and effective analytics.
  • Leveraged Python Celery for multiprocessing, optimizing data processing and significantly reducing processing time.
  • Successfully orchestrated the entire data migration process, ensuring data integrity and accuracy throughout the transfer. 
  • Played a key role in enhancing the analytics capabilities of the organization by providing a reliable and scalable data pipeline solution.
  • This project showcased expertise in handling big data, multiprocessing, and cloud services, contributing to improved data-driven decision-making and business insights.

Empowering SuperMoney: Creating and Managing Backend API (2016-2021)

  • Developed and managed the entire backend for the SuperMoney product, utilizing Spring Hibernate for seamless API integration.
  • Leveraged expertise in the Spring Framework, particularly Spring MVC, to design and implement robust and efficient REST APIs.
  • Demonstrated strong proficiency in Spring IO and Spring Boot, ensuring smooth and reliable application performance.
  • Successfully maintained and enhanced the backend system since 2016, providing ongoing support and continuous improvements.
  • This experience showcases a comprehensive understanding of backend development and Spring technologies, enabling the successful delivery and maintenance of SuperMoney's backend API.

Eventify: Empowering Real-Time Notifications with the Event Notification Engine

Objective: Pioneering a Golang Web Service: Develop an Innovative Notification System Utilizing Google Cloud Pub/Sub for Real-Time Event Communication.

  • Developed a high-performance GRPC-based web service in Golang, enabling seamless communication and data transfer between clients and the server.
  • Created a robust processor that actively listens to subscribers and efficiently sends notifications to users, ensuring real-time updates and engagement.
  • Assumed a key role in the end-to-end deployment of the system into Docker containers, optimizing scalability and resource utilization. 
  • Actively contributed to the High-Level System Design, shaping the architecture and ensuring a cohesive and efficient solution.
  • Leveraged Golang's powerful capabilities to build a reliable and performant system, resulting in a seamless user experience and enhanced notification delivery.


GPS RT Service: Efficiently Processing and Analyzing GPS Coordinates in Real-Time

  • Developed a high-performance logger service in Golang capable of handling millions of GPS coordinate writes every 10 seconds, extensively utilizing channels for efficient data processing.
  • Implemented detailed summary statistics for GPS coordinates, calculating metrics such as daily distance traveled and identifying potential home locations for users.
  • Assumed a key role in the end-to-end deployment of the system into Docker containers, ensuring seamless scalability and reliability.
  • Actively contributed to the High-Level System Design, shaping the architecture to accommodate high throughput and real-time processing requirements. T
  • This project for Supermoney showcased expertise in Golang, Docker, and data analytics, resulting in a powerful logger service that efficiently processed and analyzed vast amounts of GPS data, facilitating valuable insights and enhanced user experience.

SeatOccupancy Insights: Unleashing Real-Time Seat Data Retrieval from 5000+ Cinemas

Objective: Efficient Seat Allocation Data Retrieval: Develop a Scalable Solution to Pull Real-Time Seat Data from 5000+ Cinemas through Intelligent Pinging.

  • Successfully achieved the objective of pulling data from 5000+ cinemas for seat allocation by developing a robust seat occupancy module.
  • Implemented a smart scheduling mechanism to ping 2500 cinemas at specified intervals, capturing real-time seat occupancy data and storing it in a queuing system for further processing.
  • Designed and integrated a SeatOccupancy engine to display real-time seat allocations in cinemas, providing users with up-to-date seat availability information.
  • Effectively tackled race conditions during peak seasons when multiple users were concurrently booking seats, ensuring smooth and conflict-free seat reservations.
  • Utilized a powerful technology stack comprising Node.js, RabbitMQ, MySQL, and Golang, enabling efficient high I/O operations and seamless storage of seat allocation data.
  • This accomplishment showcases the ability to handle high-scale data processing and deliver real-time results, providing an enhanced user experience for cinema-goers. 
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