Lead engineer for large-scale LLM serving and GPU infrastructure powering tier-1 customers across multi-cloud Kubernetes platforms.

• Designed and operated large-scale LLM serving pipelines using disaggregated prefill/decode on multi-node H100 clusters (vLLM, Go, KubeRay), improving P99 latency by 60% and 4x-ing throughput for mission-critical production workloads

• Built fair-share GPU scheduling across shared clusters using Kueue, KEDA, and fractional GPU partitioning, cutting idle GPU cost by ~$200K/month while maintaining 99.9% job success and cluster availability

• Designed and deployed multi-tenant, cloud-native Kubernetes platform (AppHub IDP) with GitOps (ArgoCD, Tekton), service mesh, and disaster recovery; cut deployment time from 2 days to 15 minutes across multiple engineering teams

• Standardized end-to-end observability for AI agents using OpenTelemetry, Prometheus, and high-cardinality metrics, enabling sub-second root cause analysis of distributed failures and improving incident MTTR

• Built MLflow-backed experiment and model infrastructure on Kubernetes (PostgreSQL, MinIO) as a shared platform, automating deployments via Go CLIs and Terraform across AWS, GCP, and Azure

• Contributed open-source patches to NVIDIA Dynamo, improving distributed inference throughput and memory efficiency on A100/H100 GPU clusters

• Led ISO/IEC 27001 and SOC2 Type II compliance programs, architecting security controls across cloud infrastructure

• Developed and benchmarked distributed inference systems using Triton Inference Server, Kubeflow, and Kubernetes, achieving significant throughput improvements for LLM serving workloads in HPC environments

• Built RAG (Retrieval-Augmented Generation) pipelines using LangChain and LLMs, enabling semantic search over enterprise knowledge bases with sub-2-second query latency

• Deployed and managed multi-node GPU clusters using Slurm workload manager and HPCC systems, running MPI-based distributed training jobs with NVIDIA cuDNN and CUDA optimization

• Implemented end-to-end MLOps pipelines using Apache Airflow and MLflow, automating model training, validation, and deployment workflows on AWS and GCP

• Architected Nginx-based reverse proxy and service mesh configurations with Kubernetes, improving API gateway performance and enabling zero-downtime deployments

• Contributed to internal developer platform (IDP) tooling using Helm Charts and YAML-based Kubernetes operators, reducing onboarding time for new engineering teams

• Designed and maintained distributed microservices architecture using Java, Python, and Scala on Kubernetes, supporting enterprise clients across banking and financial services sectors

• Built and optimized CI/CD pipelines using Jenkins, Docker, and Terraform on AWS and Azure, reducing deployment time by automating infrastructure provisioning and release management

• Implemented container orchestration solutions using Kubernetes and Docker Swarm, migrating legacy monolithic applications to cloud-native microservices architecture

• Developed high-throughput data processing systems using Apache Airflow and SQL, handling large-scale ETL workflows for enterprise analytics platforms

• Engineered reliability improvements including Nginx load balancing, DNS configuration, and Single Sign-On (SSO) integrations, improving system uptime and security posture for client-facing applications

• Collaborated with cross-functional teams to deliver SOC2 compliant software systems, implementing security controls and audit logging across distributed services running on AWS and Azure infrastructure

Co-founded an Ed-tech startup focused on developing an integrated, single-window application portal to streamline access to academic and career resources for students, contributing to the platform’s design, functionality, and market readiness.

• Drove product adoption by onboarding schools, colleges and universities across regions.

• Built and led a team of 20 across tech, operations, and outreach functions.

• Made strategic partnerships with relevant resource providers, software providers and onboarded advisory board.

• Led core functions including team building, operational strategy, and technology development management.

• Led platform design, market readiness, and go-to-market planning for the student-facing application.