AI Site Reliability Engineer

CONTACT INFORMATION:

Email: ********@*****.*** Phone: 415-***-****

Address: San Francisco, California LinkedIn: IN/daemeonreiydelle

Summary

oTrue Full AI Stack architect, SRE, and developer

oDesign, improve, implement innovative AI models to enhance predictive accuracy and performance in various applications.

oOptimize algorithms to improve efficiency and reduce processing time across multiple projects, improve GPU, data pipeline utilization/throughput.

oImplement robust data training pipelines to streamline the process of model development and deployment.

oLead cross-functional teams to collaborate on complex projects, ensuring alignment and timely delivery of objectives.

oAuthored detailed technical documentation to facilitate knowledge transfer and support future development efforts. I am a plumber: architecting, tuning and improving AI stacks: Site Reliability Engineering for Data and ML Ops:

oArchitecture, configuration, and tuning of AI stacks: scalable “plumbing” (AIDataOps/MLOps in Azue, GCP, and on Prem) GPU Enabled AI, Kubernetes/Docker, high performance storage (NetApp, Pure, PowerScale), GPU optimized networks (NVidia (Kubernetes, BasePods, SuperPods, UFM, BCM, Direct RMA, ConnectX, Infiniband, NVLink/NVSwitch, etc.), structured and unstructured databases (Vectors, NoSQL, SQL), architecture and tuning of text and multimode feature detection & enrichment for RAG and MLM through LLM, architecture and optimizing of AI Stacks for production scale out, identifying bottlenecks and constraints throughout the architectures.

oPerformance tuning, optimization, of some of the largest AKS, GKS clusters, including internally for both Azure and GCP. Work with various 3rd party GPU cloud providers.

oSupporting evolving client concerns about data privacy and egress/ingress data costs for GenAI: AI appliances from Dell, AI Edge compute from Azure and GCP, 5G MEC AI, etc.

oSupporting AWS, GCP, Azure public clouds and private (bare metal, VMWare) clouds for Nvidia based architectures.

oTouched on many technologies, including various OCR (Western, Mideast, Eastern languages0 from GCP Cloud Vision to Mistral OCR), AWS ELT tools, etc.

Relevant Work History:

Anthropomorphics 2019 - Current

Generative AI Architect Lead

Kahn Ventures: (Technical Architect) September 2024 to March 2025

-Family Investment Company consulting: AI tech stack assessments of Pitch Deck Tech Stacks: review startup tech stack pitches (focus on back end, data, AI Tech Stack), suggesting strategies, alternate cluster providers, cost optimizations, shared service options, AI and other cybersecurity mitigations, infrastructure and AI data processing options, leverage of existing vs. new model development, etc. Growing focus on data quality and provider alternatives.

Dell Professional Services: (Generative AI Architect Lead) (US, UK, EU, AJP) September 2023 thru September 2024

-Support internal POCS’ and client engagements of data, AI, and Ops layers per Nvidia BasePod architecture standards (K8S, BCM, DGX, Slerm, BCM Data Mesh, etc.): installation, tuning, and upgrades client AI infrastructures: Databricks/HuggingFace/Nvidia NeMo, NIS, Bright/Base cluster manager as part of hardware and solutions sales: petrochemical, pharma, banking, higher education, etc.: support AIOps teams porting applications (image, pharma, RAG). BCM installations and upgrades. 7+ clients. Work on various POC’s with Databrick’s Mosaic AI team (Emory SPARC medicine, University of Tennessee – student outcomes)

-AI Cybersecurity/Ethical/Reliability assessments: develop the assessments with Dell Delivery AI teams and execute (ISO 42001, OWASP AI, NIST AI V2, EU AI Act, GDPR, PII, HIPAA, etc.): Support emerging hardware platforms and readiness assessments: NVidia AI Enterprise, AMD MI300/300X. Full stack Kubernetes with Run:AI. Heavy focus on data (Bias, distribution, normalization, synthetic data, etc.). Health care, banking, sports medicine.

-Customer Demos (and support other teams having performance issues in their demos): NVIDIA NVAIE, Triton Inference Server, NeMo, etc., and AMD's ROCm frameworks.

-Perform AI stack (and task) tuning for multiple client’s deliveries: stabilize/tune scalable model pretraining workflows, debug/resolve bottlenecks as 3rd level Dell AI customer facing support. Architecture, implementation, and architectural issues transformer networks like LLAMA2, FALCON, MIXTRAL, T5. GenAI emerging issues: e.g., data quality, bias, explainability, training & tuning, data tagging, chunking, model degeneration, hallucination, overreliance.

-MLOps/AIOps: Install, configure, support AI engineers: Dell Nvidia clusters (up to 200 H100 GPU’s), Nvidia UFM (Unified Fabric Manager), Infiniband setup and testing (5-20 Dell node clusters, XE9680’s thru XE8640’s) internally and at client sites (3rd level support), Kubernetes, Bright Cluster Manager, NeMo, Nvidia Inference Microservices/NI, Cephs, Slerm. Architect data meshes (unstructured/semi-structured) for multiple clients (health care, medical schools, manufacturer, midsized airport (DHS pilot), etc. Hybrid cloud and private cloud solutions (AWS, Azure, some GCP), Master data management, end-to-end governance (including reputational, legal, financial risk). Work with implementation teams to migrate, add semantic content, semantic chunking, optimize AI stacks through UX, reduce UX response times, improve AI tech stack utilization, etc. in Kubernetes (Nvidia Base, GKS, AKS, EKS), Slurm, Jupyter, etc. NiFi, Kafka, ADS, Databricks, etc.

-Delivering training classes: (Internal) Nvidia Enterprise AI for Dell, Dell Validated Design: GenAI Clusters in the Data Center (Nvidia EAI on Dell); Dell Validated Design: Data Wrangling for GenAI; (Internal) special topics for DVD for Nvidia (Hugging Face: Mistral, Run:AI; RAG w/Feedback (RLHF vs. RHAIF), DPO, Imitation Learning with DVD); Converted Data Centers: GenAI in the converged, hyperconverged, and virtualized data center; Cybersecurity for Generative AI; Digital Human: Implications for GRC, cybersecurity, and business processes.

-Dell AI Factory: R760xa, through XE9680A/Dell AI Factory with Nvidia, Nvidia BCM, Nvidia Unified Fabric Manager (UFM)/Adaptive Routing/UFM Subnet Manager, etc.; RHEL AI: OpenShift AI, IBM OS Granite LLM, Large Scale Alignment for Bots. (LAB), IBM/REL InstructLab; NVidia Omniverse OVX; PyTorch, Llama 3, PineconeDB, Pandas, NumPy, NextData, DataBricks, NER/Topic extraction, SpaCy, BigPanda, HuggingFace, MLFlow, KubeFlow, OpenAI API, etc.

Key Clients

Bank (US, EU): Nvidia EAI/NIM setup, Ethical/Reliable AI Life Cycle assessment, AI Cybersecurity assessments

Bank (US/EU): Nvidia EAI/NIM setup, AI Cybersecurity assessments

Cellular provide (EU): Dell/Nvidia Clusters for GenAI in the 5G MEC.

Cloud provider (US) AI acquisition

Health Care: two Medical School sports medicine facilities

Insurance (US): GenAI Cybersecurity assessments, Ethical/Reliable AI Governance in the life cycle assessments, etc.

Major (45,000 headcount) global energy sector consulting firm: Support the buildout of their infrastructure to support 100 16 Nvidia GPU nodes: Nvidia BCM, Kubernetes, Slerm. Tuning, assist AIOps teams in migrating to current NeMo, etc.

Military medical facility: architect shared service model for advanced AI, Digital Assistant process enhancements.

Petroleum (US): GenAI Cybersecurity assessments (Dell/Nvidia AIE/NeMo/NIM setup)

Pharma (US): GenAI Digital Humans for health care professionals (AI Cybersecurity Assessment, Nvidia AIE/NeMo/NIM setup)

Pharma (US): GenAI for LIMS (AI Cybersecurity Assessment), AI Stack improvmenets

Truck manufacturer (EU) (AI Cybersecurity Assessment, Nvidia AIE/NeMo/NIM setup)

Ligadata: Telco MSP: AI Modernization

-ML & GenAI Data (RAG) to telcos in emerging markets: Work with CTO and team to rearchitect the existing open-source big data platform to hybrid Cloud SAAS (AWS, Azure, GCP) around GenAI Data specific MLOps best practices, unstructured and semi-structured big data, support Databricks partnership in AME. Identify opportunities around 5G, IIOS, MFG 4.0 leveraging current SAAS ML pipelines. Work with CEO and Heads of Sales teams on engagements in Dubai, UAE, Nigeria, Egypt. Extend existing predictive analytics and chat diagnostic RBES’s to leverage generative AI over new data. Identify and resolve architectural and performance related issues across the client base (3rd level support). Hadoop optimization (hBase, Hive)

-Architected distributed AI/ML pipelines and managed GPU clusters (100+ GPUs) supporting large-scale ML models for telcos in emerging markets, including 5G and IIoT use cases.

-Utilized Kubernetes and cloud-native technologies (AWS, Azure) for provisioning, monitoring, and optimizing GPU clusters for predictive analytics and AI workloads.

-Led efforts in network optimization (VXLAN, fat-tree architecture) to enhance GPU performance and reduce network-related bottlenecks across distributed training systems.

-Training sessions for sales and technical teams: hybrid cloud big data, best practices for migrations, etc.

-Presales advisory for 3 Ligadata client’s AI cloud modernizations (Generative, NAG, LLM, Kubernetes, GPU scheduler Nvidia Enterprise AI, Terraform, ETL via NiFi/Kafka, etc.)

Google: AIOps

-Technical integration engineer (DevOps/AIOps) supporting Google Cloud Telco (install, tune, optimize) in pilots for Ericsson, Nokia, Casa: onboarding of their 5G Core Network Functions to GDCE Kubernetes for various telco providers (Jio, Deutche Telekom + T-Mobile, Telus, TIM (Italy, Brazil)) in 5G Core on Kubernetes (GCP Edge), supporting 5G RAN test integration and Spirent RAN load testing. Validating, load & stress testing, CI/CD automation into Google Distributed Cloud Edge (GDCE) and hybrid cloud (GDCE/GKE): Provide security, k8s configurations for NF networking, Mellanox NIC’s, performance tuning, Helm chart support: Google Telco Solutions, Kubernetes, NF Networking.

-Telco Analytics Solutions: support integration (testing) Google’s Vertex AI Cloud (GenAI RAG) to Edge for Telco RAN/MIMO and MEC optimization, training pipelines via DataProc & DataFlow on BigTables/DataProc (similar to hBase/Hadoop).

Western Digital: Disk/SSD manufacturer: AI Optimization

-AIOps: Rearchitect virtualized HPC environment to improve throughput, 3x improvement in GPU utilization, 2x faster image recognition, observability improvements in client’s fab’s. Reduced fab error rates by 3-7%, increased accuracy and utilization of cell test system by 4x, reduced expedited shipment delivery costs by 2x.

-Technical architect/deployment support: supporting new and existing AIOps/IIoT/Digital Twin deployments to Fab data centers (GKE Anthos/VMware) Worldwide; DataIQ integration; Cloudera (CDP/CDF) on Kubernetes, Architect new AI systems into Kubernetes, Rancher, Confluence Kafka, NiFi, DataIQ, CDP Spark, nVidia VMware nodes. Support Looker ETL and query performance improvements.

-Own the technical relationships with AWS, Google, Portworx, Rancher for all operational, technical and architectural asks (FAAS, SAAS, some IAAS/PAAS).

-For the test cell analytics AIOps (GenAI RAG image processing) team(s), resolved scalability and performance issues around applications running in the pods, help team to optimize containers, code, work with GCloud to identify Kubelet configuration issues, Linux kernel (CNI, Contrack, & NAT) issues, improve training, improve Looker performance, etc.

-Improve application responsiveness by enhancing Kubernetes operators for AIOps training, inference exception eventing, monitoring, and complex Spark/Kafka/NiFi ELT needs.

-AIOps improvements for GKE (TensorFlow: TFU’s & GPU’s; VMWare/GKE, HP HPC), NiFi, Kafka, Hadoop/Hbase, MongoDB, Elasticsearch for predictive analytics and event driven ML inference for various digital twin applications.

11 Fab’s (3 US, Israel, India, 2 Thailand, 2 China, 2 Japan) with collocated data centers, running 12 clusters of Anthos Kubernetes (GKE), ~2k K8S nodes, 1M pods, support subset of global business apps running in AWS and GCP, moving to Onprem Anthos, with global ML primarily in GCP, and general business in AWS: except for Fab based operations collocated due to 1-2 petabytes of daily data per fab, client is multicloud native (no data centers). VMware, Rancher, Portworx, EMC, NetApp. Spark, NiFi, Snowflake, DataBricks, DataIQ, Bitbucket, AWS EKS, GCP GKE, GKE Anthos VMWare, De-scheduler, Envoy, Prometheus, Splunk, CloudStrike, Grafana, ELK, Goldilocks, Fairwinds, Java, Go, Python, TensorFlow, VMWare, GKS Tesla GPU pod scheduling, Aero, Bitbucket, Artifactory, Airflow, Kafka (Confluent), Bitnami, AWS Redshift, Snap Logic (ERTL), EMR, Jenkins, Spinnaker, MongoDB, PostgreSQL, MySQL, Elasticsearch, AWS Redshift, Cilium, Pega (Supply Chain Analytics), EKS.

Microsoft Professional Services: Major Telecoms Company: Cloud/AI Modernization

-Initially SME for Open-Source Big Data ecosystem (DSS, AI/ML) on Azure, part of team that implemented AKS, then supporting AKS engagements: Digital transformation of 5500 applications (75,000 systems) to Azure. AKS (Kubernetes) Subject Matter Expert team, heavy focus on securing Kubernetes/containers. Architect 400 remote clusters of Remote Kubernetes (5G MasterCore/EdgeCompute), K8S real time intrusion detection, SME supporting the migration to AKS (Terraform + Helm via DevOps pipelines – ML/DevOps) of existing AI, Azure Machine Learning, & “big data” components: Hadoop, HD Insights, Cassandra, Spark, Airflow, Snowflake, Packer/Docker, AKS and Azure SAAS (Azure AI, ML, Functions, Container Svc., IoT, DataBricks), HDinsights (Hadoop/hBase), etc. Leverage Terraform and Istio, Azure APIM, Linode (multicloud Kubernetes and IAAS). Work with the evolving Azure Databricks offering to support client’s GenAI migrations. Support Terraform based Azure Containers and Azure Kubernetes Service based on Microsoft + Hashicorp developed Terraform tooling for Azure DevOps CI, extending for security in depth (security as code) controls (SELinux, ACL controls on load balancers, ingress & egress controllers, advanced configuration of Nginx, L5/L7 proxies, VPC peering, etc.

-AIOps application migration: architecture, implementation/optimization support migrating to Azure Kubernetes (nVidia GPU’s), scheduler migration (Run:AI, Airflow), Terraform, for client’s wireline and wireless predictive analytics, various chatbots, and sentiment analysis applications. AKS, Azure Machine Learning/AML Studio, Run:AI for Kubernetes, Airflow, Terraform.

-5G: Architect and modify our Azure Terraform templates (GitHub Actions/Runners) to support Azure for Operators Cloud (IAAS, Kubernetes/AKS, Azure Container Services, Azure Modular Data Center (5G Edge), Azure API Management (AKS Services)), 5G MasterCore/EdgeCompute, Kubernetes real time IDS for edge compute.

Walmart Online

-AIOps/SRE Observability: Assess, recommend, and assist in bringing observability best practices to Walmart Online including the ML and DSS data flows. Improve utilization of web infrastructure under stress by 2x through improvements in CI test case coverage, Stress test automation at scale. Similar improvements to HPC utilization under load by 3x. (Hadoop, HBase, Cassandra, Spark, Airflow, nVidia T100’s, Kubernetes, Azure, Prometheus, Logstash to Splunk, Grafana), Hadoop (Horton Works), Cassandra.

-SRE/PM: Pilot to Production model to improve slow AI/Data Science applications: work to accelerate AI projects to move to production in Azure, GCP. Leverage Azure Kubernetes, AKS Cloud Scheduler: resulting in higher quality suggestions, better ad insertion fees, 15% increase from partner ad revenue. H2O.ai (MLOps, Driverless AI), Looker. Azure ML, GCP BigTables, BigQuery, CloudSQL. ATP, Supply Chain. SAP integration (R/3, SCM, CM, BW).

Avanade (Accenture/Microsoft JV) (Senior Architect to Practice Lead) 2018 - 2019

-Develop Avanade COE for Site Reliability & Observability, Create and grow the practice, leveraging existing clients’ scalability and reliability issues to build referenceable clients in the SRE/Observability space. In addition to evangelizing DevOps and SRE, develop and grow the MLOps and ML Site Reliability practices areas around Azure and 3rd party offerings. Provide presales engagement support, SOW response, and post-sales architecture, debug, and implementation support for Avanade and Accenture engagements related to big data ELT, ML CI/CD, DSS, practical AI, predictive ML, sentiment analysis, Azure Chatbots. Deploy and improve observability with improved tooling (Splunk, ELK, Grafana, etc. as appropriate to client landscapes)

-Accenture AI & Analytics: Develop best practices for MLOps (AI Transformers & Deep Learning), ELT, support presentations, participate in internal and client POC’s. MLOps & ML SRE architecture deliveries. Support pilots and POC’s for Microsoft’s new Azure Databricks offering.

-Accenture Digital Transformations Risk Management Office: participate in turnarounds of problem digital transformations; Risk Management Office reviews of various projects.

-Technical lead of (15+) Pilot to Production engagements: AI Ops, SecOps, DevOps Velocity, Agile vs. Waterfall, AKS/Docker migration, SAP on Azure, Data Lakes/Edge Analytics/BI/AI assisted BI/Azure HDInsights/Databricks. Led to 5 ($15M) new engagements and an estimated $8M uplift to existing engagements.

-15+ Microsoft Professional services Pilot to Production engagements via Education Delivery: Azure for Agile DevOps, Azure CI/CD, Azure IoT, Azure Big Data & Analytics Services/Synapse, etc.

-10+ Azure SAP HANA Migration Reviews & assessments to improve SAP DevOps. 15% increase in pipeline within 90 days of sales team training

-Identified an employee satisfaction improvement opportunity: shortfall in new-tech learning by existing staff when hiring outside for new talents (e.g. I was hired for my Big Data Ops SRE experience): Work with CFO, HR and key practice leads to start a 3% learning objective KPI that had the highest response from staff worldwide of any program ever.

-Develop training and consulting material around CyberSecurity in depth for Docker and Kubernetes clusters and components.

Key engagements:

Leading Retail Azure transformation Azure: reduction of net new cross silo code development by 25%.

Leading Cloud (MSFT/Accenture JV) provider (20+ engagement workshops & classes) for DevOps at scale

Biopharma (US) Azure Big Data, Azure IoT, Big Data DevOps. Make LIMS, JV projects, R&D data fully searchable online (approx. 3PB of documents now searchable via semantic analysis)

Pharma (UK) Azure Big Data, Centrify, Azure Data Lake. From paper and raw pdf to fully semantically indexed document search improvements across all JV’s. ~2.5PB

Leading hospitality: MuleSoft ERP/AnyPoint, SAP R/3 to AWS, Kafka (HDF & Confluent), Informatica, Cassandra, Hybrid DevOps, AKS (Kubernetes, Docker). Reduced AWS spend by 3% while increasing throughput by 15%

Health Care: Azure Data Lake, Azure BI, etc. for improved practitioner UX. MedTech and MD misclassification reduction by 75% resulting in substantial improvement in insurance provider payment turnaround

US Cellphone merger: presales RFP: responsible for DevOps/MLOps capabilities. Won contract ($180M)

AMS (Big Data Architect to Practice Lead) (US, EU) 2013-2018

-As the 3rd hire in the big data architecture practice, leverage my experience as a developer of Hadoop (Yahoo) to solve big data scalability business process issues for clients, grow the AWS provider partnership as one of the first combined Technology+Consulting partners.

Key engagements:

Architecture Software (4000 subscriptions) (AWS)

Calif. Dept. of Education

Daimler Bosch ADAS

EMC/Pivotal

Fortuna Liebherr (EU)

Google Mapping acquisition (Waymo/Kubernetes)

Google Satellite Imaging Acquisition (Google Maps/Kubernetes + Borg)

IBM Watson Earth

JPMChase

Mobile banking provider (65% of all banks worldwide) (AWS)

Music Streaming Service (AWS)

Petroleum Company: Seismic Imaging

Petroleum Pipeline & Storage Services (AWS)

Petroleum Services Company (AWS)

Pinterest

RedCloud/Amazon

Surveillance Video (AWS)

Uber

Verizon

Video Enhanced Security (AWS)

Vodaphone

Ansys (Senior Architect) 2009 - 2013

-Develop “Pilot to Production” micro project model to rapidly grow simulation software capabilities into new areas: SAP DevOps (BasisTechnologies partner), workshops, presentations: Given responsibility for 3rd party integration for SAP solutions (support presales, proposals, POC’s, and SAP BASIS delivery architect) for SAP Enterprise Portal (TopTier) engagements, and Salesforce (Force.com).

-Work with AWS and SAP to certify SAP for non-production systems (Hybrid Cloud), then work with AWS to market the solution as an exclusive AWS partner (with SAP AG). All engagements were P2P based growth. No big bang or major brown/green field projects!

Key engagements:

Aerospace (rotary wing) manufacturer – MLOps – AWS ($5M, $25M ROI first year)

Chemical Distributor (SAP DevOps AWS)

Computer equipment manufacturer

CPG conglomerate

Electric car manufacturer

Global Security Services Provider

Grocery Store Chain (Western US)

Major American car financing bank

Major computer storage & virtualization company

Major Medical equipment manufacturer

Major networking company: DevOps/Solution Architect (SAP AWS)

Major telecoms manufacturer - MLOps

Major US Bank

Major Utility (UK, US, Canada - $140M) – DevOps/MLOps – project turnaround ($60M)

Medical Electronics Manufacturer (Mexico, Brazil)

Multinational Beverage CPG

Multinational glass and ceramics manufacturer

Petroleum Field Services Provider (SAP DevOps AWS, MLOps)

Pharma (SAP PDC/AWS)

Semiconductor equipment manufacturer

US computer equipment manufacturer

US Government Agency

World’s largest vehicle manufacturer

Experience prior to 2009

AWS cloud, Agile, DevOps transformations, SAP Cloud, SAP HANA, SAP Technical through Practice Lead/CIO Advisory Practice: business and cloud transformation consulting (US, Asia, Middle East, UK, Germany, Ireland, Italy, France)

Teaching:

California Institute of Technology 2021/2022

Professor: School of Information Science Adjunct Professor

–Lectures: Cybersecurity, DevSecOps Practices

–Seminars: Cloud Transformation Advisory Lectures, Security in Depth, SRE/DevSecOps, Site Reliability Engineering, Multi-cloud Strategies

–Cybersecurity Supply Chain Lectures: Assess various Open-Source projects for code coverage, OWAS, CISA, etc. testing using AWS Code, Azure DevOps.

Education:

Shaftesbury University: London, England

Bachelor of Science – Computer Science 1991

Certification/Training:

Agile Certified Practitioner (DSDM)

Agile SAFe:

AWS Certification: Certified AWS Architect (Accenture)

Azure Certifications: Azure Trainer, Azure Architect, Azure Security Engineer, Azure DevOps, Azure AI/Big Data, SAP HANA on Azure

Checkpoint Firewall Engineer

Cisco Certified Network Engineer

Cisco PIX Engineer

Cisco UCS/vBlock (Cisco + NetApp) Certified Engineer

DevOps (OneOps: Azure DevOps, GCP DevOps)

GCP Certification: Certified Accenture GCP Architect

HortonWorks Certified HDP & HDF Architect, Admin

IBM Systems Engineer Certification – IBM AIX, HA/CMP

Microsoft Azure Partner Program Certified Solution Architect

NVidia Partner Certified Associate: AI/DC, Mellanox, Accelerated Computing Fundamentals, HPC containers, Data Science Workflows, GenAI, GNN, RNN, Bright Clusters

Superpod/Basepod, Accelerated Computing, BCM, Cuda, etc.

Network Appliance Certified Engineer

Oracle RAC/DBA certification

PMI (PMP) – no longer active

SAP HANA Partner Engineer

SAP HANA: Technical Certification

VMWare Certified Engineer

Classes Taught:

Agile processes for Big Data Transformations

AWS Infrastructure (Big Data DevOps, EMR (Hadoop/Spark), Redshift, Terraform)

Azure: 15+ Certification Classes/Training Workshops: Azure DevOps, Azure Kubernetes Service, Azure DataLakes, Azure Big Data, Azure Technology, Azure Architect, Azure IoT, Azure Security, SAP HANA on Azure

Hadoop, Hive, Spark, Spark Streaming, Kafka/Flume

SAP LVM, SAP LaMa, DevOps for SAP (SAP Cloud, Azure, AWS), SAP Basis, SAP GRC, BO GRC

University: Cloud Cybersecurity (2021)

University: Cloud Fundamentals (2021

University: DevSecOps (2021, 2022)

University: Computer Science (2021, 2022)

Citizenship: U.S.

Security Clearance: US Top Secret/EBI (expired, resubmission required)

Tools:

Accenture Applied Intelligence Platform (AI, ML, ETL/ELT)

Agile processes for Big Data Transformations: Sprints, Kanban

Agile/DSDM

Apache Airflow, Flume, Hadoop, Hive, Spark, Spark Streaming, Kafka

Application & End User Monitoring (Dynatrace, New Relic, Splunk, SolarWinds)

Aqua IDS/IPS

AT&T Alien Labs Kubernetes IDS

AWS Big Data DevOps, EMR (Hadoop/Spark), Redshift, Terraform, DevOps, EC2, ECS, Lambda, API Gateway, Cloud Front, Cloud Watch, Kinesis, AMI

Azure: (Advanced Data Factory, Agile, AI, Big Data, CI/CD, Containers/Docker, AKS, Azure Private 5G Core, ADO/Azure DevOps)

CosmosDB, Data Lake Analytics, Data Lakes & Data Lake Store, Database for MySQL, Database migration service, DataBricks (Data Lake & Delta Engine), Azure DevOps, Docker, Edge, Event Bus/Event Hub, Functions, HDInsights (Hadoop Spark Kafka), IoT, Kubernetes/AKS, Machine Learning, PowerShell/Automation, Security, Stream Analytics, System Center

Bamboo

Bash

Cassandra (Apache, DataStax)

CircleCI

CloudBees

Confluence

Docker (Compose, Swarm, Packer)

Drupal

DSDM (Agile)

Elastic Search (also Logstash, Kibana, ELK)

Falco (K8S IDS)

Git, GitHub, GitLab

GNU Linux: RedHat, Ubuntu, SELinux, Alpine

GoLang/Go

Google Cloud/GCP (Anthos/VMWare, BigTables, BigQuery, Kubernetes/GKE, DevOps, DataFlow, DataProc, Anthos, Istio, Velostrata, Google Spanner, Google Compute Engine, GCE, Kubernetes, GKE, Big query, Cloud SQL, Cloud Run, Digital Run, App Engine, GCP Edge, Looker, Single Store DB)

H2O: ML DevOps, Driverless AI, beta of Q

HipChat

HortonWorks HDP & HDF Architect, Admin: Hadoop, Kafka, Spark, NiFi

Java

JavaScript, Node.js, NPM, etc.

Jira

Jupyter Notebooks, JupyterLab

Kafka (Azure, Hortonworks, Confluent, IBM, Kafka Proxies)

Kibana

Kubernetes (AWS, GCP, Azure), Anthos, Swarm, Helm, CNI’s, Cloud Cluster Manager, Multicloud, sidecars, ingress/egress controllers, Etcd, Istio, LinkerD, Calico/anetd

Linode (AWS, Azure, Linode Kubernetes)

Logstash

Matlab

Medusa, Nginx

MITRE ATT&CK Framework

MongoDB, MySQL, MariaDB, CosmosDB

MuleSoft

NVIDIA architecture certification, DGX Superpod/Basepod, Accelerated Computing, BCM, Cuda, etc.

Network Appliance

NiFi (Apache, Hortonworks)

OneOps: Azure/GCP/OpenStack

Onyx Cloud Automation (Consona)

Oracle Cloud (Oracle Cloud services, Oracle Autonomous Database, Oracle Autonomous Linux, various Oracle Cloud Applications)

Oracle RAC/DBA, ODB, GoldenGate

Packer

Palo Alto Networks, Prisma Cloud

PMI (PMP) – no longer active

PostMan

Project Management Professional

Python, Python ML libraries (varying currency, includes Pandas, PySpark, scikit-learn, NumPy, Theano, PyTorch, Tensorflow Python, Keras)

R language

Salesforce, Force.Com

SAP BASIS, GRC (CUP, SPM, ERM), Business Objects GRC, Strategic Enterprise Management: SEM/BCS, SEM/BPS, AFS, Fashion Vertical

SAP Cloud, SAP on Azure, SAP AWS, SAP on GCP

SAP HANA & S/4, BW for HANA: AWS, GCP, Azure, Very Large HANA instances

SAP HANA & SAP S/4 for Azure

Security/Threat tools (Attivo Networks, Cyberfusion/MFT-IS, CyberMDX, CyberX, Firedome, SecuriThings, Vintella, WebSeal)

Selenium

Simian Army (Chaos Monkey)

Site Reliability Engineering (SRE) SLO/SLI/Observability

Six Sigma Green Belt

Slack

Snowflake

Spark

Splunk Enterprise, Splunk Security, AppDynamics, Sumo Logic, SolarWinds, DataDog, Medusa Enterprise)

Tensorflow

Terraform (Azure w/ARM templates, AWS, GCP, Oracle Cloud)

VMWare Pivotal Cloud, VMWare vSphere on AWS & GCP

WordPress

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