Professional Overview
Senior AI / MLOps Engineer with 8+ years of experience building production-grade AI systems, scalable backend platforms, and cloud-native ML infrastructure across startups and Fortune 500 environments. Specialized in LLM-powered applications, multi-agent orchestration, RAG systems, real-time inference pipelines, MLOps workflows, and AI platform
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and scaled enterprise-grade GenAI and LLM systems
- Architected and operated a real-time multi-agent AI platform
- Designed and implemented a multi-tier SQL caching and knowledge graph system
- Developed an AI-powered trace debugging assistant
- Designed scalable distributed inference pipelines
Key Achievements / KPIs
- Reduced end-to-end voice interaction latency by 90%
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Led ML and LLM platform engineering initiatives for a cybersecurity SaaS
- Designed and implemented end-to-end ML and LLM pipelines
- Productionized training and inference pipelines
- Owned and operated GKE-based ML infrastructure,
- Deployed centralized observability and monitory (Grafana, Loki)
Key Achievements / KPIs
- Improved data processing performance by 12×
- Built CI/CD automation pipelines for reliable ML service deployment workflows.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Productionized forecasting models from Jupyter notebooks
- Designed and deployed ML forecasting pipelines
- Implemented end-to-end ELT pipelines
- Established CI/CD and GitOps workflows for ML services,
Areas of Expertise
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.