AI Solution Architect

Seasoned solutions architect with over 20 years in IT, including 7 years leading machine-learning projects and a decade of GDPR and information-security expertise, delivering high-impact AI & compliance solutions

Proven senior team-lead who drives end-to-end data-strategy initiatives, designing cloud-native architectures on Azure and AWS, implementing data-mesh concepts, and orchestrating big-data migrations for pharma and automotive clients

Expert in full-stack development and IoT platforms, proficient with Java, Python, Scala, and modern frameworks, building scalable micro-services, containerized deployments with Docker/Kubernetes, and integrating SAP data via APIs

Effective pre-sales and stakeholder collaborator, shaping AI/ML proof-of-concepts, knowledge-graph solutions, and edge-AI models on MCU devices, while ensuring HIPAA and GDPR compliance across healthcare and life-science projects

Defined the Enterprise Data Strategy for e-Mobility in line with Data Mesh principles and FedRAMP compliance

Participated in Architecture Board meetings and provided consulting on Data-Driven Development and designing Data Products

Designed the Cloud Data Architecture for Charging Stations, EVSEs and CPOs following OCA’s OCPP 2.0.1 framework

Led AI/ML proof-of-concepts for predictive maintenance of charging stations

Developed quick proof-of-concepts and reference architectures using OpenSearch, QuickSight, Athena, PySpark, AWS Kinesis Data Firehose and Glue pipelines

Defined the enterprise data analytics strategy and BI dashboarding

Actively participated in pre-sales activities and responded to customer RFx documents

Drove customer engagements and owned end-to-end solution delivery of IT/Digital projects

Developed rapid proof-of-concepts and reference architectures for digital solutions

Provided consulting on Data Mesh, Data-Driven Development and designing Data Products

Oversaw cloud-based solution architecture for end-to-end IT applications in automotive, healthcare and life-sciences domains

Performed a Big Data migration of Life-Sciences data from a Cloudera platform to Amazon S3 and an EMR cluster, handling MIMIC-III and other EMR datasets

Used Amazon S3, Parquet files, PySpark and EMR

Created Data Pipelines for ingesting data from legacy and modern SAP systems into Azure Data Mesh via Azure Databricks

Designed data models, pipelines, and assets, published enterprise data for various data products

Integrated Azure ADLS Gen2, Delta Lake, and Snowflake

Technologies used included SAP Data Services, Azure Databricks, Delta Lake, Azure ADLS Gen2, and Cosmos DB

Designed the architecture for a Knowledge Graph application using on-premise SQL Server data

Data was replicated to Azure SQL Server, then ingested into AWS S3, transformed into RDF files and loaded into AWS Neptune for ontology generation

Implemented one-time migration and delta updates

Technologies included Azure SQL Server replication, Azure Functions, AWS Lambda (Python), AWS S3, AWS Neptune, SPARQL, RDF and OWL

Developed a Sales Product Search application

Ingested SAP data from MuleSoft APIs into Azure Redis and Azure SQL Server

Ingested event data from Azure Service Bus into Azure PostgreSQL

Created micro-services on PostgreSQL and SQL Server using Nest.js and Next.js

Contributed to a federated Graph Gateway using Apollo

Participated in IT pre-sales activities, engaging with customers to understand requirements and pain points

Prepared cloud-based solution architectures for end-to-end IoT applications and platforms

Oversaw and supported AI/ML development and deployment in cloud-based production environments

Implemented proof-of-concepts using deep-learning libraries on IoT Edge and gateways

Developed a TensorFlow Lite model for anomaly detection in electronic motors of various appliances using STM32 ultra-low-power MCUs

The proof-of-concept embeds a tiny machine-learning model on the edge of MCUs; the MCU relays messages to a system controller, which forwards IoT messages to the cloud

Developed a self-learning AI engine on Azure Cloud using patient data and wearable IoT device data, leveraging Infermedica APIs as knowledge systems

Delivered IoT platform architecture, fall-detection and symptom-diagnosis ML algorithms, and Azure Machine Learning code and models

Ensured compliance with HIPAA and GDPR for protected user data

Designed and rolled out architecture for an edge-based and cloud-based application to detect ARDS, using machine-learning models

Shaped use cases based on generated ventilator, EMR and EHR data

Structured application infrastructure to ensure HIPAA and GDPR compliance

Implemented machine-learning inferencing with Keras and TensorFlow

Prepared architecture for a next-generation AI platform using NVIDIA DeepStream and NVIDIA Triton Inference Server

Defined approach for porting and migrating existing neural-network models to NVIDIA edge devices and Triton Server

Designed a scalable, cloud-agnostic micro-services architecture with Docker containers, Kubernetes and Helm charts