# Meghamsh R > AI/ML Engineer | GenAI & LLM Infrastructure | Scaling AI Systems Across GPUs & Cloud | Ex-NVIDIA, Accenture | 5+ YOE Location: United States, United States Profile: https://flows.cv/meghamsh AI/ML Engineer with 5+ years of experience building and scaling enterprise-grade AI systems at Scale AI, NVIDIA, and Accenture. I specialize in designing end-to-end ML pipelines, deploying large-scale inference systems, and developing GenAI/LLM solutions that drive measurable business impact. At Scale AI, I improved ML pipeline efficiency by 40% and enhanced model performance through RLHF-based evaluation systems. At NVIDIA, I optimized large-scale GPU inference systems, increasing throughput while reducing costs by 30%. Previously at Accenture, I delivered scalable AI solutions that improved model accuracy and reduced production issues significantly. My expertise spans distributed systems, MLOps, cloud-native architectures (AWS, Kubernetes), and high-performance AI infrastructure. I bring strong cross-functional leadership and a data-driven mindset to solve complex problems and accelerate AI adoption at scale. I’m passionate about building impactful AI systems, especially in Generative AI and LLM ecosystems. πŸ“© Open to connecting with recruiters and teams working on cutting-edge AI/ML challenges. ## Work Experience ### Machine Learning Engineer @ Scale AI Jan 2025 – Present | New York, United States ● Owned and scaled ML data pipelines that significantly boosted annotation efficiency (~40% faster) while improving data reliability across enterprise workflows. ● Built intelligent LLM evaluation systems with RLHF loops, driving measurable improvements in model alignment and real-world performance. ● Reimagined data lifecycle processes (ingestion to validation), cutting delays by 35% and enabling faster, higher-quality model training. ● Acted as a bridge between engineering, product, and business teams to translate AI requirements into scalable solutions. ● Introduced data-driven decision systems (KPIs & dashboards) to improve visibility into model and operational performance. ● Drove adoption of GenAI-first workflows, accelerating experimentation and innovation across teams. ● Designed scalable, cloud-native ML systems ensuring reliability, security, and high availability. ● Played a key role in roadmap execution, prioritization, and continuous delivery in fast-paced AI environments ### Software Engineer- Machine Learning @ NVIDIA Jan 2024 – Jan 2025 | Sunnyvale, CA ● Led deployment of high-performance AI inference systems across large GPU clusters, improving efficiency while reducing infrastructure costs. ● Designed smarter GPU scheduling and workload distribution, boosting utilization and reducing latency in production systems. ● Built optimized inference pipelines using CUDA & TensorRT, enabling faster and scalable AI model execution. ● Developed microservices-based AI systems with Kubernetes, ensuring seamless scaling and reliability. ● Partnered with cross-functional teams to deliver production-ready AI solutions aligned with business needs. ● Focused on performance tuning and system optimization, continuously improving throughput and response times. ● Delivered APIs, documentation, and tools that simplified AI adoption across teams. ● Contributed to product direction through data insights, roadmap discussions, and execution planning. ### Software Engineer @ Accenture Jan 2020 – Jan 2023 | India ● Delivered scalable AI/ML solutions for enterprise clients, improving model performance and enabling real-world deployment at scale. ● Built end-to-end ML pipelines with strong focus on reliability, monitoring, and explainability, reducing production issues significantly. ● Converted research ideas into production-ready systems, helping teams move faster from experimentation to deployment. ● Collaborated across teams to drive delivery using Agile methodologies and structured execution. ●Designed cloud-based AI systems using modern tools and MLOps practices. ● Enabled teams to make better decisions through data insights, dashboards, and performance tracking. ●Mentored junior engineers and contributed to building a strong, collaborative team culture. ## Education ### Master's degree in Information Technology Saint Louis University ### Bachelor of Technology - BTech in Computer science engineering Guru Nanak Institutions Technical Campus ## Contact & Social - LinkedIn: https://linkedin.com/in/meghamshr - Portfolio: https://meghamsh-r.github.io/Meghamsh-Portfolio.github.io/ --- Source: https://flows.cv/meghamsh JSON Resume: https://flows.cv/meghamsh/resume.json Last updated: 2026-04-17