Professional Overview
Senior engineer with 13+ years building software end to end, from production AI/ML infrastructure to full-stack platforms. I architect the systems that run AI in production: LLM and RAG pipelines, model serving, containerized deployment, GPU/compute observability, and edge-to-cloud inference.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Architected AI documentation and smart prompt platforms connecting LLM workflows
- Built cloud-native microservices and containerized CI/CD shipping across DEV/UAT
- Designed production LLM pipelines covering model calls, data flow, post-processi
- Implemented secure multi-tenant architecture with auth, RBAC, and observability
Key Achievements / KPIs
- Automated enterprise documentation and knowledge discovery across multiple codeb
- Shipped 4 production AI platforms across DEV/UAT/PROD on Kubernetes with full CI
- Built multi-model orchestration across OpenAI and Claude with prompt governance
Areas of Expertise
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
