JAGDEEP PHOOLKUMAR

Application Modernization using AWS AI Suite:

• Delivered Amazon Q Developer and AWS Transform demonstrations to C-suite executives and technical leaders across multiple enterprise customers, showcasing AI-powered code transformation that accelerates migration by 4x and reduces Windows licensing costs by up to 40% through automated .NET Framework to cross-platform modernization

• Conducted deep dive technical sessions on Agentic development workflows featuring Amazon Q Developer for autonomous code generation and multi-file feature implementation, Kiro IDE for specification-driven development (requirements → design → implementation), and AWS Transform for automated legacy code modernization of 1M+ lines

• Demonstrated Model Context Protocol (MCP)-powered automation enabling seamless tool connectivity across development workflows, including autonomous agents for code reviews, unit test generation, and documentation creation that reduce manual development effort

• Showcased full-stack Windows modernization capabilities including SQL Server to Aurora PostgreSQL migration, ASP.NET transformation, and automated dependency analysis that preserves business-critical logic while eliminating technical debt

• Enabled enterprise customers to validate migration acceleration and cost optimization strategies through hands-on demonstrations, consistently receiving positive feedback from technical and executive leadership for demonstrating depth in GenAI solutions and practical transformation approaches

Building MLOps and Data Platform Solutions:

• Built production-ready RLHF Training Platform POC on Amazon EKS, delivering scalable reinforcement learning infrastructure for distributed training workloads that supported $1M opportunity pipeline and published technical blog enabling broader customer adoption

• Led strategic $1.8M Cloudera-to-AWS migration, designing end-to-end solution for on-premises NiFi 1.x to NiFi 2.x modernization on AWS, navigating complexities across 400+ data flows while creating Level 300 technical content on Kubernetes-native data architectures for enterprise-scale transformation

Thought Leadership in Agentic AI:

• Delivered AI-powered migration demonstrations at AWS Innovate reaching 118 attendees with 4.6/5 CSAT, showcasing Amazon Q Developer and AWS Transform capabilities for VMware and .NET modernization with two-step AI-assisted transformation workflows that accelerate migration by 4x and reduce costs by up to 40%

• Conducted AWS Transform for .NET workshop with 35 partner participants, demonstrating AI-powered transformation tools and microservice extraction capabilities that enable automated legacy code modernization and cross-platform migration

• Built AI-powered migration calculator using multi-agent architecture (Amazon Bedrock API and Perplexity API) that reduced migration setup from weeks to hours through automated TCO analysis, receiving management recognition for accelerating customer migration workflows

• Co-developed and premiered AWS AI chip demonstrations showcasing LLM deployment using NVIDIA GPU, AWS Graviton, Inferentia, and Trainium at Data & AI Days (Sydney, Melbourne, Auckland), subsequently adopted across APJ regional summits in India, Korea, ASEAN, and Japan

• Mentored specialist through Awesome Builder program, establishing independent ML on EKS capability that enabled team member to deliver customer engagements and contribute to data platform modernization initiatives

Strategic GenAI/ML Platform Wins:

• Secured $3M MAP deal as APJ's first major GenAI win, serving as embedded technical thought partner from prototype to production, benchmarking H100, A100, L40S, and Inferentia2 for vLLM-based multi-language QWEN models—customer selected for AWS Global Accelerator AI Program (1 of 80 globally)

• Delivered $500K EKS-based GenAI platform, architecting self-managed LLM infrastructure with Kubeflow and Knative enabling independent deployment of multiple proprietary models through containerized orchestration and serverless inference

• Closed $300K MLOps modernization reducing deployment time from weeks to hours, designing MLflow and Kubeflow on EKS framework transforming legacy manual ML infrastructure into automated CI/CD pipelines

• Secured $200K contract through Llama fine-tuning cost optimization POC, evaluating Llama Factory on Trainium versus Unsloth on H100/A100 with throughput, convergence, and TCO analysis demonstrating 50% cost reduction potential

Enterprise Data & ML Platform Transformation:

• Led $16M enterprise data platform migration for major financial institution, serving as embedded technical advisor conducting 12 comprehensive design sessions across Spark, Trino, Airflow, and ML infrastructure while establishing trusted advisor relationship with bank executives

• Won 3 of 4 benchmark categories in competitive evaluation retaining $12M compete opportunity, leading Amazon EMR vs. Databricks performance analysis, identifying 5 critical resiliency gaps in self-managed EKS platform, and delivering production-ready Spark + Airflow on EKS architecture blueprint

Technical Go-to-Market Leadership:

• Led EC2 compute go-to-market strategy across ASEAN region, cultivating C-suite relationships and delivering technical enablement content through workshops, summits, and webinars—achieved 5x revenue growth within two years, secured 5 lighthouse customer wins, and garnered public references across multiple industries

• Developed and executed Data/AIML on Kubernetes go-to-market strategy across APJ region, securing leadership sponsorship, conducting extensive customer outreach, and orchestrating creation of data modernization design patterns published as open-source resources—led proof-of-concept executions through strategic partnerships and Professional Services resulting in 4 net-new multi-million dollar migrations from on-premises Hadoop to AWS

Technical Thought Leadership & Community Impact:

• Created Data on EKS Blueprint adopted by Slack, Roblox, Grab, and AWS ProServe globally, delivering open-source reference architecture integrating Spark and Airflow on EKS with security, monitoring, and cost optimization—recognized as global SME by DoEKS team

• Reached 10K+ developers through AWS Blogs publications, authoring technical content including "Serving OpenAI Whisper with Ray Serve and Gradio on Amazon EKS powered by AWS Neuron" demonstrating production-grade AI inference patterns

• Earned AWS Neuron Expert competency (2024) through Inferentia2 optimization work, leading Llama 3.x benchmarking with vLLM, implementing production-grade inference pipelines using Ray Serve and AWS Neuron SDK, and contributing product feedback that improved SDK capabilities

• Trained 160 attendees at AWS re:Invent 2023 Data on EKS Workshop, delivering hands-on training on Spark, Airflow, and ML workloads on Kubernetes for enterprise-scale data platform modernization

Organizational Enablement & Mentorship:

• Achieved 250% of enablement target training 180 TFC members and 200+ customers across APJC, delivering EMR Bootcamp (103 SAs/TAMs), conducting EKS Operational Excellence Roadshow across 4 cities (356 attendees), and delivering 40+ technical sessions reaching 2,500+ attendees with 4.5+ CSAT

• Mentored 3 specialists to independent contributor status in ML on EKS, Data on EKS, and Streaming on EKS, expanding team capacity through hands-on customer engagements and knowledge transfer that enabled autonomous delivery of enterprise data platform modernization initiatives

Data Analytics & ML Platform Migration to AWS:

• Led strategic migration of on-premises Data and ML platform to AWS, reducing annual operating costs from $5M to $1M (80% savings) through cloud-native architecture optimization

• Architected and designed comprehensive cloud-native Data Analytics and ML platform featuring Apache Airflow on ECS Fargate for orchestration, Amazon EMR for batch processing, and Amazon SageMaker for ML workflows—designed end-to-end platform covering networking, security, reliability, and operational excellence for Credit Risk and Wholesale Risk teams

• Conducted POCs validating migration strategy and techincal design, orchestrating SAS jobs on Airflow, evaluating ETL with Apache Spark, implementing ML workflows on SageMaker, and demonstrating visualization with QuickSight and Tableau—secured Design Review Board approval through technical presentations

• Led team of 5 cloud engineers building deployment automation using Terraform IaC, delivering production-ready platform for enterprise-scale analytics and ML workloads

Cloud Migration & Microservices Modernization:

• Led cloud migration planning and microservices modernization strategy, architecting hybrid cloud infrastructure deploying new microservices on AWS while maintaining legacy monolithic applications in AT&T Data Centers

• Designed production-scale Cassandra distributed NoSQL clusters supporting 100K peak orders with high availability, fault tolerance, and low-latency reads/writes for mission-critical order processing workloads

• Led multifaceted teams (infrastructure, DevOps, performance engineers) orchestrating modernization to cloud-based ecosystems with Kubernetes and microservices architecture

• Designed scalable infrastructure serving 40M monthly active users with 20K peak redemptions, supporting $2B annual influenced revenue and ensuring seamless scaling for peak traffic with proactive incident service delivery for three consecutive years

• Established performance and observability standards across organization, conducting comprehensive system benchmarking and code profiling to optimize application and infrastructure performance

• Evaluated and recommended monitoring solutions (Dynatrace, New Relic, AppDynamics) as standard toolset, developing automated analytics framework with monitoring dashboards using ELK, Grafana, and Splunk

• Ensured business performance SLAs were consistently met through systematic infrastructure and application code tuning, maintaining 99.9%+ uptime for revenue-critical systems

SOA-Based Insurance Application Infrastructure:

• Designed and managed SOA-based insurance application infrastructure using IBM WebSphere suite with mainframe integration, conducting comprehensive CPU and memory benchmarks to size hardware for application and database servers

• Developed capacity models to accommodate future trends and growth, enabling seamless scalability and proactively identifying and resolving performance bottlenecks within application and infrastructure