Senior Backend/AI Engineer at NVIDIA (2025-01 – 2026-01)
Worked as a Senior AI/Data Engineer on AI and data projects, delivering scalable enterprise solutions using Rust, Python, distributed systems, LLM, ML, and cloud technologies. Owned and developed several core features from scratch across the full lifecycle of AI systems
- Designed and built from scratch a real-time recommendation and ranking system using Python and Rust-based ML pipelines with Azure OpenAI services, covering feature engineering, model training, hyperparameter tuning, low-latency inference, and production monitoring
- Developed autonomous demand forecasting and predictive analytics models supporting supply chain planning, capacity optimization, and strategic business decisions, leveraging high-performance Rust components for distributed computation
- Built personalized customer segmentation, propensity scoring, and lifecycle optimization frameworks from scratch to improve targeting, retention, and business KPIs
- Implemented automated feature engineering and data transformation pipelines powering multiple downstream ML and LLM workflows, ensuring data quality and consistency
- Architected scalable RAG-style knowledge retrieval systems and AI-assisted analytics workflows leveraging Azure OpenAI services, including full orchestration of AI agent reasoning and decision-making workflows inspired by LangChain
- Developed distributed ETL/ELT pipelines and warehouse data models for large-scale multi-source analytics, designing critical high-performance components in Rust to ensure throughput, reliability, and safety
- Delivered production-grade real-time and batch inference services with containerized deployments on AWS cloud infrastructure, optimizing for low latency, scalability, and fault tolerance
- Established end-to-end MLOps/LLMOps workflows including CI/CD automation, model versioning, drift detection, automated retraining, and monitoring across distributed systems
- Designed scalable cloud infrastructure using serverless and containerized architectures to support high-performance AI workloads
- Optimized large-scale SQL analytics processing, distributed data system performance, and cross-team integration with Product, Analytics, and Engineering teams
AI/Data Engineer at UiPath (2019-07 – 2024-11)
Worked as an AI/Data Engineer, contributing to an enterprise-scale Agentic AI platform focused on intelligent automation and decision intelligence.
- Owned and developed from scratch a multi-agent orchestration framework, enabling autonomous agents to plan, reason, and execute workflows across enterprise systems
- Designed and implemented intelligent task routing and prioritization models using ranking and predictive algorithms to optimize automation performance
- Built end-to-end ML to support agent decision-making and outcome prediction
- Developed real-time and batch inference services to power low-latency agent responses within enterprise workflows
- Architected scalable ETL/ELT pipelines and data models to process large-scale operational and user interaction data
- Established MLOps standards including CI/CD for ML pipelines, automated retraining, model versioning, monitoring, and drift detection
- Designed AWS-native, containerized and serverless infrastructure to ensure high availability, scalability, and resilient ML service deployment
- Optimized large-scale SQL data processing and feature stores to support multiple AI use cases across business units
- Collaborated cross-functionally with Product, Engineering, and Automation teams, owning AI-driven features from architecture design through production release
Software Engineer at Infosys (2015-11 – 2019-04)
Developed scalable and efficient AI-driven solutions with focus on data pipelines and machine learning models.
- Developed scalable and efficient AI-driven solutions, building and maintaining robust data pipelines to support machine learning models and analytics workflows
- Designed and implemented data engineering processes, including data collection, cleaning, transformation, and integration from multiple sources to enable reliable model training and business intelligence
- Built and optimized machine learning models to extract insights, automate decision-making, and improve predictive capabilities across the platform
- Collaborated with cross-functional teams using version control and MLOps best practices to manage code, experiments, and model deployments in a structured and reproducible manner
- Conducted advanced data analysis and performance monitoring using analytics tools and statistical methods to uncover trends, improve model accuracy, and enhance user engagement
- Maintained comprehensive technical documentation covering data architectures, model assumptions, APIs, experiment results, and best practices to ensure knowledge sharing and long-term maintainability
