AI Engineer at IBM — building production RAG pipelines, AI agents, and GenAI systems for enterprise clients.
AI Engineer at IBM where I architect production-grade Generative AI systems for IBM customers and business partners.
Beyond my work at IBM, I maintain Ad Astra AI—a technical blog documenting production-ready AI engineering projects, sharing deep-dives on building, deploying, and scaling real-world AI systems. My flagship project, SentiWiki AI, is an end-to-end agentic RAG system deployed on AWS ECS for querying ESA Copernicus Sentinel mission documentation.
Previously, one year as a Solutions Architect at Huawei gave me deep expertise in cloud architectures, which I've further validated through AWS, Databricks, and Anthropic certifications.
Currently also an Associate Professor in Machine Learning at UAX University, where I designed and deliver comprehensive curricula for Machine Learning I & Machine Learning II courses to 20+ undergraduate students in the AI & Computation degree program.
An AI-assisted replay for finished Formula 1 weekends: interactive track maps, standings, tyre and pit strategy timelines, weather overlays, race-control messages, and short natural-language takeaways.
Built to turn timing data and session context into something you can explore end-to-end after the chequered flag.
An agentic RAG stack for Earth observation: Copernicus Sentinel mission docs indexed for retrieval, with orchestrated agents planning multi-step queries and grounding answers in cited passages.
Production-minded RAG — structured ingestion, retrieval with guardrails, and agent loops that favour clarity and traceability.
Advanced architecture for production Claude systems — agent design, tool orchestration, evaluation, and enterprise deployment patterns.
View Certification Details →Foundational skills for building applications with Claude — API integration, prompting, and developer workflows.
View Certification Details →Architecture patterns, tool use, and production-minded agent design with Claude.
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LLM-enabled solutions on Databricks: RAG, Vector Search, Model Serving, MLflow, Unity Catalog.
View Certification Details →Batch/streaming pipelines, Delta Lake, warehouses, lakes, ETL, PySpark (Databricks Intelligence Platform).
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Building, training, tuning, and deploying ML on AWS (two years' experience validated).
View Certification Details →Secure, scalable, cost-optimised designs across AWS services.
View Certification Details →Foundational AI/ML and AWS implementation practices.
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