● 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

● 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.

● 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.